Dangerous ground: One-year-old infants are sensitive to peril in other agents’ action plans
Shari Liu
October 25, 2021
detach("package:dplyr", unload = TRUE)
library(dplyr)
# read in data
wide <- read.csv(file = "peril_data_deid.csv", header = TRUE)
head(wide)## X reliability sex subj agem experiment exp cost video_quality
## 1 NA NA m S1_1 10.5 LUTS.Exp.1 Barriers NA
## 2 NA NA f S1_10 9.9 LUTS.Exp.1 Barriers NA
## 3 NA NA m S1_11 9.8 LUTS.Exp.1 Barriers NA
## 4 NA NA m S1_12 9.9 LUTS.Exp.1 Barriers NA
## 5 NA NA f S1_13 10.2 LUTS.Exp.1 Barriers NA
## 6 NA NA f S1_14 10.0 LUTS.Exp.1 Barriers NA
## audio_quality device highchair HV_side first_test first_fam
## 1 NA NA left HV HL
## 2 NA NA left LV LH
## 3 NA NA left HV HL
## 4 NA NA right LV LH
## 5 NA NA right HV LH
## 6 NA NA right LV HL
## first_test_deeper_side control_deeper_side control_firstevent control_1
## 1
## 2
## 3
## 4
## 5
## 6
## control_2 fam1 fam2 fam3 fam4 fam5 fam6 test1 test2 test3 test4 avg_fam
## 1 60 60 60 60 59.12 9.75 32.8 6.38 14.2 60 51
## 2 60 60 50.06 23.11 13.93 14.26 54.04 20.82 11.82 19.46 37
## 3 60 60 48.73 60 29.89 36.65 52.47 18.15 34.18 30.82 49
## 4 60 60 60 37.15 13.48 51.68 33.26 45.95 5.65 14.07 47
## 5 60 60 60 40.67 28.57 10.77 7.22 6.86 6.5 <NA> 43
## 6 60 11.14 56.06 60 32.97 28.77 17.37 9.95 12.22 6.71 41
## sum_fam testavg_lower testavg_higher lower1 lower2 higher1 higher2
## 1 309 33.2 23.5 6.38 60 32.8 14.2
## 2 221 32.9 20.1 54.04 11.82 20.82 19.46
## 3 295 24.5 43.3 18.15 30.82 52.47 34.18
## 4 282 19.5 30.0 33.26 5.65 45.95 14.07
## 5 260 6.9 6.9 6.86 <NA> 7.22 6.5
## 6 249 14.8 8.3 17.37 12.22 9.95 6.71
## control_shallow control_deep
## 1
## 2
## 3
## 4
## 5
## 6str(wide)## 'data.frame': 286 obs. of 40 variables:
## $ X : int NA NA NA NA NA NA NA NA NA NA ...
## $ reliability : int NA NA NA NA NA NA NA NA NA NA ...
## $ sex : chr "m" "f" "m" "m" ...
## $ subj : chr "S1_1" "S1_10" "S1_11" "S1_12" ...
## $ agem : num 10.5 9.9 9.8 9.9 10.2 ...
## $ experiment : chr "LUTS.Exp.1" "LUTS.Exp.1" "LUTS.Exp.1" "LUTS.Exp.1" ...
## $ exp : chr "" "" "" "" ...
## $ cost : chr "Barriers" "Barriers" "Barriers" "Barriers" ...
## $ video_quality : int NA NA NA NA NA NA NA NA NA NA ...
## $ audio_quality : num NA NA NA NA NA NA NA NA NA NA ...
## $ device : chr "" "" "" "" ...
## $ highchair : int NA NA NA NA NA NA NA NA NA NA ...
## $ HV_side : chr "left" "left" "left" "right" ...
## $ first_test : chr "HV" "LV" "HV" "LV" ...
## $ first_fam : chr "HL" "LH" "HL" "LH" ...
## $ first_test_deeper_side: chr "" "" "" "" ...
## $ control_deeper_side : chr "" "" "" "" ...
## $ control_firstevent : chr "" "" "" "" ...
## $ control_1 : chr "" "" "" "" ...
## $ control_2 : chr "" "" "" "" ...
## $ fam1 : chr "60" "60" "60" "60" ...
## $ fam2 : chr "60" "60" "60" "60" ...
## $ fam3 : chr "60" "50.06" "48.73" "60" ...
## $ fam4 : chr "60" "23.11" "60" "37.15" ...
## $ fam5 : chr "59.12" "13.93" "29.89" "13.48" ...
## $ fam6 : chr "9.75" "14.26" "36.65" "51.68" ...
## $ test1 : chr "32.8" "54.04" "52.47" "33.26" ...
## $ test2 : chr "6.38" "20.82" "18.15" "45.95" ...
## $ test3 : chr "14.2" "11.82" "34.18" "5.65" ...
## $ test4 : chr "60" "19.46" "30.82" "14.07" ...
## $ avg_fam : num 51.5 36.9 49.2 47 43.3 ...
## $ sum_fam : num 309 221 295 282 260 ...
## $ testavg_lower : num 33.19 32.93 24.49 19.46 6.86 ...
## $ testavg_higher : num 23.5 20.14 43.33 30.01 6.86 ...
## $ lower1 : chr "6.38" "54.04" "18.15" "33.26" ...
## $ lower2 : chr "60" "11.82" "30.82" "5.65" ...
## $ higher1 : chr "32.8" "20.82" "52.47" "45.95" ...
## $ higher2 : chr "14.2" "19.46" "34.18" "14.07" ...
## $ control_shallow : chr "" "" "" "" ...
## $ control_deep : chr "" "" "" "" ...wide.info <- wide %>% filter(exp == "Exp.1b"|
exp == "Exp.2b"|
exp == "Exp.3b")
table(wide.info$sex)##
## f m
## 52 50# convert into long format
long <- gather(wide, type, look, testavg_lower:control_deep)
str(long)## 'data.frame': 2288 obs. of 34 variables:
## $ X : int NA NA NA NA NA NA NA NA NA NA ...
## $ reliability : int NA NA NA NA NA NA NA NA NA NA ...
## $ sex : chr "m" "f" "m" "m" ...
## $ subj : chr "S1_1" "S1_10" "S1_11" "S1_12" ...
## $ agem : num 10.5 9.9 9.8 9.9 10.2 ...
## $ experiment : chr "LUTS.Exp.1" "LUTS.Exp.1" "LUTS.Exp.1" "LUTS.Exp.1" ...
## $ exp : chr "" "" "" "" ...
## $ cost : chr "Barriers" "Barriers" "Barriers" "Barriers" ...
## $ video_quality : int NA NA NA NA NA NA NA NA NA NA ...
## $ audio_quality : num NA NA NA NA NA NA NA NA NA NA ...
## $ device : chr "" "" "" "" ...
## $ highchair : int NA NA NA NA NA NA NA NA NA NA ...
## $ HV_side : chr "left" "left" "left" "right" ...
## $ first_test : chr "HV" "LV" "HV" "LV" ...
## $ first_fam : chr "HL" "LH" "HL" "LH" ...
## $ first_test_deeper_side: chr "" "" "" "" ...
## $ control_deeper_side : chr "" "" "" "" ...
## $ control_firstevent : chr "" "" "" "" ...
## $ control_1 : chr "" "" "" "" ...
## $ control_2 : chr "" "" "" "" ...
## $ fam1 : chr "60" "60" "60" "60" ...
## $ fam2 : chr "60" "60" "60" "60" ...
## $ fam3 : chr "60" "50.06" "48.73" "60" ...
## $ fam4 : chr "60" "23.11" "60" "37.15" ...
## $ fam5 : chr "59.12" "13.93" "29.89" "13.48" ...
## $ fam6 : chr "9.75" "14.26" "36.65" "51.68" ...
## $ test1 : chr "32.8" "54.04" "52.47" "33.26" ...
## $ test2 : chr "6.38" "20.82" "18.15" "45.95" ...
## $ test3 : chr "14.2" "11.82" "34.18" "5.65" ...
## $ test4 : chr "60" "19.46" "30.82" "14.07" ...
## $ avg_fam : num 51.5 36.9 49.2 47 43.3 ...
## $ sum_fam : num 309 221 295 282 260 ...
## $ type : chr "testavg_lower" "testavg_lower" "testavg_lower" "testavg_lower" ...
## $ look : chr "33.19" "32.93" "24.49" "19.46" ...# log transform looks
long$look <- as.numeric(as.character(long$look))
long$loglook <- log(long$look)
# set levels for different kinds of looks
long$type <- factor(long$type)
# subset averaged looks across test pairs (2 observations per participant) and control events
long.avg <- long %>%
filter(type == "testavg_higher" | type == "testavg_lower" | type == "control_deep" | type =="control_shallow") %>%
separate(type, into=c("phase", "type"), sep="_")%>%
# add age groups (relevant for Experiments 1-3)
mutate(agegroup = as.factor(case_when(agem < 12 ~ "younger",
agem > 12 ~ "older")))
long.avg$type <- factor(long.avg$type)
long.avg$phase <- factor(long.avg$phase)
long.avg$exp <- factor(long.avg$exp)
long.avg$sex <- relevel(as.factor(long.avg$sex), ref = "m")
exp1.avg <-dplyr::filter(long.avg, exp == "Exp.1")
exp2.avg <-dplyr::filter(long.avg, exp == "Exp.2")
exp3.avg <-dplyr::filter(long.avg, exp == "Exp.3")
exp1b.avg <-dplyr::filter(long.avg, exp == "Exp.1b")
exp2b.avg <-dplyr::filter(long.avg, exp == "Exp.2b")
exp3b.avg <-dplyr::filter(long.avg, exp == "Exp.3b")
tenm <- rbind(exp1b.avg, exp2b.avg, exp3b.avg)
tenm ## X reliability sex subj agem experiment exp cost video_quality
## 1 NA 1 f S5_1 10.4 RISK10 Exp.1b Risk NA
## 2 NA 1 f S5_2 10.4 RISK10 Exp.1b Risk NA
## 3 NA 1 f S5_3 10.3 RISK10 Exp.1b Risk NA
## 4 NA 1 m S5_4 10.4 RISK10 Exp.1b Risk NA
## 5 NA 0 m S5_5 10.1 RISK10 Exp.1b Risk NA
## 6 NA 0 f S5_6 10.5 RISK10 Exp.1b Risk NA
## 7 NA 1 f S5_7 10.1 RISK10 Exp.1b Risk NA
## 8 NA 0 f S5_8 9.8 RISK10 Exp.1b Risk NA
## 9 NA 1 f S5_9 10.3 RISK10 Exp.1b Risk NA
## 10 NA 1 m S5_10 9.9 RISK10 Exp.1b Risk NA
## 11 NA 1 m S5_11 10.6 RISK10 Exp.1b Risk NA
## 12 NA 1 f S5_12 10.1 RISK10 Exp.1b Risk NA
## 13 NA 0 m S5_13 9.8 RISK10 Exp.1b Risk NA
## 14 NA 0 f S5_14 10.6 RISK10 Exp.1b Risk NA
## 15 NA 0 f S5_15 10.2 RISK10 Exp.1b Risk NA
## 16 NA 0 f S5_16 9.6 RISK10 Exp.1b Risk NA
## 17 NA 0 m S5_17 10.0 RISK10 Exp.1b Risk NA
## 18 NA 0 m S5_18 10.2 RISK10 Exp.1b Risk NA
## 19 NA 1 m S5_19 9.8 RISK10 Exp.1b Risk NA
## 20 NA 1 f S5_20 10.4 RISK10 Exp.1b Risk NA
## 21 NA 0 m S5_21 9.6 RISK10 Exp.1b Risk NA
## 22 NA 0 m S5_22 10.5 RISK10 Exp.1b Risk NA
## 23 NA 0 m S5_23 9.6 RISK10 Exp.1b Risk NA
## 24 NA 0 m S5_24 10.1 RISK10 Exp.1b Risk NA
## 25 NA 0 m S5_25 10.2 RISK10 Exp.1b Risk NA
## 26 NA 0 m S5_26 10.0 RISK10 Exp.1b Risk NA
## 27 NA 0 m S5_27 10.3 RISK10 Exp.1b Risk NA
## 28 NA 0 f S5_28 9.7 RISK10 Exp.1b Risk NA
## 29 NA 1 f S5_29 10.0 RISK10 Exp.1b Risk NA
## 30 NA 1 m S5_30 10.4 RISK10 Exp.1b Risk NA
## 31 NA 1 f S5_31 9.8 RISK10 Exp.1b Risk NA
## 32 NA 1 m S5_32 10.6 RISK10 Exp.1b Risk NA
## 33 NA 1 f S5_1 10.4 RISK10 Exp.1b Risk NA
## 34 NA 1 f S5_2 10.4 RISK10 Exp.1b Risk NA
## 35 NA 1 f S5_3 10.3 RISK10 Exp.1b Risk NA
## 36 NA 1 m S5_4 10.4 RISK10 Exp.1b Risk NA
## 37 NA 0 m S5_5 10.1 RISK10 Exp.1b Risk NA
## 38 NA 0 f S5_6 10.5 RISK10 Exp.1b Risk NA
## 39 NA 1 f S5_7 10.1 RISK10 Exp.1b Risk NA
## 40 NA 0 f S5_8 9.8 RISK10 Exp.1b Risk NA
## 41 NA 1 f S5_9 10.3 RISK10 Exp.1b Risk NA
## 42 NA 1 m S5_10 9.9 RISK10 Exp.1b Risk NA
## 43 NA 1 m S5_11 10.6 RISK10 Exp.1b Risk NA
## 44 NA 1 f S5_12 10.1 RISK10 Exp.1b Risk NA
## 45 NA 0 m S5_13 9.8 RISK10 Exp.1b Risk NA
## 46 NA 0 f S5_14 10.6 RISK10 Exp.1b Risk NA
## 47 NA 0 f S5_15 10.2 RISK10 Exp.1b Risk NA
## 48 NA 0 f S5_16 9.6 RISK10 Exp.1b Risk NA
## 49 NA 0 m S5_17 10.0 RISK10 Exp.1b Risk NA
## 50 NA 0 m S5_18 10.2 RISK10 Exp.1b Risk NA
## 51 NA 1 m S5_19 9.8 RISK10 Exp.1b Risk NA
## 52 NA 1 f S5_20 10.4 RISK10 Exp.1b Risk NA
## 53 NA 0 m S5_21 9.6 RISK10 Exp.1b Risk NA
## 54 NA 0 m S5_22 10.5 RISK10 Exp.1b Risk NA
## 55 NA 0 m S5_23 9.6 RISK10 Exp.1b Risk NA
## 56 NA 0 m S5_24 10.1 RISK10 Exp.1b Risk NA
## 57 NA 0 m S5_25 10.2 RISK10 Exp.1b Risk NA
## 58 NA 0 m S5_26 10.0 RISK10 Exp.1b Risk NA
## 59 NA 0 m S5_27 10.3 RISK10 Exp.1b Risk NA
## 60 NA 0 f S5_28 9.7 RISK10 Exp.1b Risk NA
## 61 NA 1 f S5_29 10.0 RISK10 Exp.1b Risk NA
## 62 NA 1 m S5_30 10.4 RISK10 Exp.1b Risk NA
## 63 NA 1 f S5_31 9.8 RISK10 Exp.1b Risk NA
## 64 NA 1 m S5_32 10.6 RISK10 Exp.1b Risk NA
## 65 NA 1 f S5_1 10.4 RISK10 Exp.1b Risk NA
## 66 NA 1 f S5_2 10.4 RISK10 Exp.1b Risk NA
## 67 NA 1 f S5_3 10.3 RISK10 Exp.1b Risk NA
## 68 NA 1 m S5_4 10.4 RISK10 Exp.1b Risk NA
## 69 NA 0 m S5_5 10.1 RISK10 Exp.1b Risk NA
## 70 NA 0 f S5_6 10.5 RISK10 Exp.1b Risk NA
## 71 NA 1 f S5_7 10.1 RISK10 Exp.1b Risk NA
## 72 NA 0 f S5_8 9.8 RISK10 Exp.1b Risk NA
## 73 NA 1 f S5_9 10.3 RISK10 Exp.1b Risk NA
## 74 NA 1 m S5_10 9.9 RISK10 Exp.1b Risk NA
## 75 NA 1 m S5_11 10.6 RISK10 Exp.1b Risk NA
## 76 NA 1 f S5_12 10.1 RISK10 Exp.1b Risk NA
## 77 NA 0 m S5_13 9.8 RISK10 Exp.1b Risk NA
## 78 NA 0 f S5_14 10.6 RISK10 Exp.1b Risk NA
## 79 NA 0 f S5_15 10.2 RISK10 Exp.1b Risk NA
## 80 NA 0 f S5_16 9.6 RISK10 Exp.1b Risk NA
## 81 NA 0 m S5_17 10.0 RISK10 Exp.1b Risk NA
## 82 NA 0 m S5_18 10.2 RISK10 Exp.1b Risk NA
## 83 NA 1 m S5_19 9.8 RISK10 Exp.1b Risk NA
## 84 NA 1 f S5_20 10.4 RISK10 Exp.1b Risk NA
## 85 NA 0 m S5_21 9.6 RISK10 Exp.1b Risk NA
## 86 NA 0 m S5_22 10.5 RISK10 Exp.1b Risk NA
## 87 NA 0 m S5_23 9.6 RISK10 Exp.1b Risk NA
## 88 NA 0 m S5_24 10.1 RISK10 Exp.1b Risk NA
## 89 NA 0 m S5_25 10.2 RISK10 Exp.1b Risk NA
## 90 NA 0 m S5_26 10.0 RISK10 Exp.1b Risk NA
## 91 NA 0 m S5_27 10.3 RISK10 Exp.1b Risk NA
## 92 NA 0 f S5_28 9.7 RISK10 Exp.1b Risk NA
## 93 NA 1 f S5_29 10.0 RISK10 Exp.1b Risk NA
## 94 NA 1 m S5_30 10.4 RISK10 Exp.1b Risk NA
## 95 NA 1 f S5_31 9.8 RISK10 Exp.1b Risk NA
## 96 NA 1 m S5_32 10.6 RISK10 Exp.1b Risk NA
## 97 NA 1 f S5_1 10.4 RISK10 Exp.1b Risk NA
## 98 NA 1 f S5_2 10.4 RISK10 Exp.1b Risk NA
## 99 NA 1 f S5_3 10.3 RISK10 Exp.1b Risk NA
## 100 NA 1 m S5_4 10.4 RISK10 Exp.1b Risk NA
## 101 NA 0 m S5_5 10.1 RISK10 Exp.1b Risk NA
## 102 NA 0 f S5_6 10.5 RISK10 Exp.1b Risk NA
## 103 NA 1 f S5_7 10.1 RISK10 Exp.1b Risk NA
## 104 NA 0 f S5_8 9.8 RISK10 Exp.1b Risk NA
## 105 NA 1 f S5_9 10.3 RISK10 Exp.1b Risk NA
## 106 NA 1 m S5_10 9.9 RISK10 Exp.1b Risk NA
## 107 NA 1 m S5_11 10.6 RISK10 Exp.1b Risk NA
## 108 NA 1 f S5_12 10.1 RISK10 Exp.1b Risk NA
## 109 NA 0 m S5_13 9.8 RISK10 Exp.1b Risk NA
## 110 NA 0 f S5_14 10.6 RISK10 Exp.1b Risk NA
## 111 NA 0 f S5_15 10.2 RISK10 Exp.1b Risk NA
## 112 NA 0 f S5_16 9.6 RISK10 Exp.1b Risk NA
## 113 NA 0 m S5_17 10.0 RISK10 Exp.1b Risk NA
## 114 NA 0 m S5_18 10.2 RISK10 Exp.1b Risk NA
## 115 NA 1 m S5_19 9.8 RISK10 Exp.1b Risk NA
## 116 NA 1 f S5_20 10.4 RISK10 Exp.1b Risk NA
## 117 NA 0 m S5_21 9.6 RISK10 Exp.1b Risk NA
## 118 NA 0 m S5_22 10.5 RISK10 Exp.1b Risk NA
## 119 NA 0 m S5_23 9.6 RISK10 Exp.1b Risk NA
## 120 NA 0 m S5_24 10.1 RISK10 Exp.1b Risk NA
## 121 NA 0 m S5_25 10.2 RISK10 Exp.1b Risk NA
## 122 NA 0 m S5_26 10.0 RISK10 Exp.1b Risk NA
## 123 NA 0 m S5_27 10.3 RISK10 Exp.1b Risk NA
## 124 NA 0 f S5_28 9.7 RISK10 Exp.1b Risk NA
## 125 NA 1 f S5_29 10.0 RISK10 Exp.1b Risk NA
## 126 NA 1 m S5_30 10.4 RISK10 Exp.1b Risk NA
## 127 NA 1 f S5_31 9.8 RISK10 Exp.1b Risk NA
## 128 NA 1 m S5_32 10.6 RISK10 Exp.1b Risk NA
## 129 NA 1 f 01-MR 10.1 MR10 Exp.2b Risk NA
## 130 NA 0 m 03-MR 10.1 MR10 Exp.2b Risk NA
## 131 NA 1 f 12-MR 10.3 MR10 Exp.2b Risk NA
## 132 NA 0 f 13-MR 10.1 MR10 Exp.2b Risk NA
## 133 NA 0 m 15-MR 9.8 MR10 Exp.2b Risk NA
## 134 NA 0 f 17-MR 10.3 MR10 Exp.2b Risk NA
## 135 NA 0 m 18-MR 9.7 MR10 Exp.2b Risk NA
## 136 NA 1 m 19-MR 10.3 MR10 Exp.2b Risk NA
## 137 NA 1 m 21-MR 9.5 MR10 Exp.2b Risk NA
## 138 NA 0 f 22-MR 9.5 MR10 Exp.2b Risk NA
## 139 NA 0 f 28-MR 10.3 MR10 Exp.2b Risk NA
## 140 NA 1 f 29-MR 9.8 MR10 Exp.2b Risk NA
## 141 NA 0 f 30-MR 9.6 MR10 Exp.2b Risk NA
## 142 NA 1 f 35-MR 10.0 MR10 Exp.2b Risk NA
## 143 NA 0 f 36-MR 9.7 MR10 Exp.2b Risk NA
## 144 NA 0 f 37-MR 10.1 MR10 Exp.2b Risk NA
## 145 NA 0 m 38-MR 9.0 MR10 Exp.2b Risk NA
## 146 NA 1 f 41-MR 9.9 MR10 Exp.2b Risk NA
## 147 NA 1 m 42-MR 9.9 MR10 Exp.2b Risk NA
## 148 NA 0 f 43-MR 9.8 MR10 Exp.2b Risk NA
## 149 NA 0 f 53-MR 10.3 MR10 Exp.2b Risk NA
## 150 NA 0 f 55-MR 10.2 MR10 Exp.2b Risk NA
## 151 NA 1 m 58-MR 9.5 MR10 Exp.2b Risk NA
## 152 NA 1 f 60-MR 10.5 MR10 Exp.2b Risk NA
## 153 NA 1 f 61-MR 9.6 MR10 Exp.2b Risk NA
## 154 NA 0 m 62-MR 10.3 MR10 Exp.2b Risk NA
## 155 NA 0 m 63-MR 10.4 MR10 Exp.2b Risk NA
## 156 NA 1 m 64-MR 10.3 MR10 Exp.2b Risk NA
## 157 NA 0 m 65-MR 9.9 MR10 Exp.2b Risk NA
## 158 NA 1 m 66-MR 10.1 MR10 Exp.2b Risk NA
## 159 NA 1 f 01-MR 10.1 MR10 Exp.2b Risk NA
## 160 NA 0 m 03-MR 10.1 MR10 Exp.2b Risk NA
## 161 NA 1 f 12-MR 10.3 MR10 Exp.2b Risk NA
## 162 NA 0 f 13-MR 10.1 MR10 Exp.2b Risk NA
## 163 NA 0 m 15-MR 9.8 MR10 Exp.2b Risk NA
## 164 NA 0 f 17-MR 10.3 MR10 Exp.2b Risk NA
## 165 NA 0 m 18-MR 9.7 MR10 Exp.2b Risk NA
## 166 NA 1 m 19-MR 10.3 MR10 Exp.2b Risk NA
## 167 NA 1 m 21-MR 9.5 MR10 Exp.2b Risk NA
## 168 NA 0 f 22-MR 9.5 MR10 Exp.2b Risk NA
## 169 NA 0 f 28-MR 10.3 MR10 Exp.2b Risk NA
## 170 NA 1 f 29-MR 9.8 MR10 Exp.2b Risk NA
## 171 NA 0 f 30-MR 9.6 MR10 Exp.2b Risk NA
## 172 NA 1 f 35-MR 10.0 MR10 Exp.2b Risk NA
## 173 NA 0 f 36-MR 9.7 MR10 Exp.2b Risk NA
## 174 NA 0 f 37-MR 10.1 MR10 Exp.2b Risk NA
## 175 NA 0 m 38-MR 9.0 MR10 Exp.2b Risk NA
## 176 NA 1 f 41-MR 9.9 MR10 Exp.2b Risk NA
## 177 NA 1 m 42-MR 9.9 MR10 Exp.2b Risk NA
## 178 NA 0 f 43-MR 9.8 MR10 Exp.2b Risk NA
## 179 NA 0 f 53-MR 10.3 MR10 Exp.2b Risk NA
## 180 NA 0 f 55-MR 10.2 MR10 Exp.2b Risk NA
## 181 NA 1 m 58-MR 9.5 MR10 Exp.2b Risk NA
## 182 NA 1 f 60-MR 10.5 MR10 Exp.2b Risk NA
## 183 NA 1 f 61-MR 9.6 MR10 Exp.2b Risk NA
## 184 NA 0 m 62-MR 10.3 MR10 Exp.2b Risk NA
## 185 NA 0 m 63-MR 10.4 MR10 Exp.2b Risk NA
## 186 NA 1 m 64-MR 10.3 MR10 Exp.2b Risk NA
## 187 NA 0 m 65-MR 9.9 MR10 Exp.2b Risk NA
## 188 NA 1 m 66-MR 10.1 MR10 Exp.2b Risk NA
## 189 NA 1 f 01-MR 10.1 MR10 Exp.2b Risk NA
## 190 NA 0 m 03-MR 10.1 MR10 Exp.2b Risk NA
## 191 NA 1 f 12-MR 10.3 MR10 Exp.2b Risk NA
## 192 NA 0 f 13-MR 10.1 MR10 Exp.2b Risk NA
## 193 NA 0 m 15-MR 9.8 MR10 Exp.2b Risk NA
## 194 NA 0 f 17-MR 10.3 MR10 Exp.2b Risk NA
## 195 NA 0 m 18-MR 9.7 MR10 Exp.2b Risk NA
## 196 NA 1 m 19-MR 10.3 MR10 Exp.2b Risk NA
## 197 NA 1 m 21-MR 9.5 MR10 Exp.2b Risk NA
## 198 NA 0 f 22-MR 9.5 MR10 Exp.2b Risk NA
## 199 NA 0 f 28-MR 10.3 MR10 Exp.2b Risk NA
## 200 NA 1 f 29-MR 9.8 MR10 Exp.2b Risk NA
## 201 NA 0 f 30-MR 9.6 MR10 Exp.2b Risk NA
## 202 NA 1 f 35-MR 10.0 MR10 Exp.2b Risk NA
## 203 NA 0 f 36-MR 9.7 MR10 Exp.2b Risk NA
## 204 NA 0 f 37-MR 10.1 MR10 Exp.2b Risk NA
## 205 NA 0 m 38-MR 9.0 MR10 Exp.2b Risk NA
## 206 NA 1 f 41-MR 9.9 MR10 Exp.2b Risk NA
## 207 NA 1 m 42-MR 9.9 MR10 Exp.2b Risk NA
## 208 NA 0 f 43-MR 9.8 MR10 Exp.2b Risk NA
## 209 NA 0 f 53-MR 10.3 MR10 Exp.2b Risk NA
## 210 NA 0 f 55-MR 10.2 MR10 Exp.2b Risk NA
## 211 NA 1 m 58-MR 9.5 MR10 Exp.2b Risk NA
## 212 NA 1 f 60-MR 10.5 MR10 Exp.2b Risk NA
## 213 NA 1 f 61-MR 9.6 MR10 Exp.2b Risk NA
## 214 NA 0 m 62-MR 10.3 MR10 Exp.2b Risk NA
## 215 NA 0 m 63-MR 10.4 MR10 Exp.2b Risk NA
## 216 NA 1 m 64-MR 10.3 MR10 Exp.2b Risk NA
## 217 NA 0 m 65-MR 9.9 MR10 Exp.2b Risk NA
## 218 NA 1 m 66-MR 10.1 MR10 Exp.2b Risk NA
## 219 NA 1 f 01-MR 10.1 MR10 Exp.2b Risk NA
## 220 NA 0 m 03-MR 10.1 MR10 Exp.2b Risk NA
## 221 NA 1 f 12-MR 10.3 MR10 Exp.2b Risk NA
## 222 NA 0 f 13-MR 10.1 MR10 Exp.2b Risk NA
## 223 NA 0 m 15-MR 9.8 MR10 Exp.2b Risk NA
## 224 NA 0 f 17-MR 10.3 MR10 Exp.2b Risk NA
## 225 NA 0 m 18-MR 9.7 MR10 Exp.2b Risk NA
## 226 NA 1 m 19-MR 10.3 MR10 Exp.2b Risk NA
## 227 NA 1 m 21-MR 9.5 MR10 Exp.2b Risk NA
## 228 NA 0 f 22-MR 9.5 MR10 Exp.2b Risk NA
## 229 NA 0 f 28-MR 10.3 MR10 Exp.2b Risk NA
## 230 NA 1 f 29-MR 9.8 MR10 Exp.2b Risk NA
## 231 NA 0 f 30-MR 9.6 MR10 Exp.2b Risk NA
## 232 NA 1 f 35-MR 10.0 MR10 Exp.2b Risk NA
## 233 NA 0 f 36-MR 9.7 MR10 Exp.2b Risk NA
## 234 NA 0 f 37-MR 10.1 MR10 Exp.2b Risk NA
## 235 NA 0 m 38-MR 9.0 MR10 Exp.2b Risk NA
## 236 NA 1 f 41-MR 9.9 MR10 Exp.2b Risk NA
## 237 NA 1 m 42-MR 9.9 MR10 Exp.2b Risk NA
## 238 NA 0 f 43-MR 9.8 MR10 Exp.2b Risk NA
## 239 NA 0 f 53-MR 10.3 MR10 Exp.2b Risk NA
## 240 NA 0 f 55-MR 10.2 MR10 Exp.2b Risk NA
## 241 NA 1 m 58-MR 9.5 MR10 Exp.2b Risk NA
## 242 NA 1 f 60-MR 10.5 MR10 Exp.2b Risk NA
## 243 NA 1 f 61-MR 9.6 MR10 Exp.2b Risk NA
## 244 NA 0 m 62-MR 10.3 MR10 Exp.2b Risk NA
## 245 NA 0 m 63-MR 10.4 MR10 Exp.2b Risk NA
## 246 NA 1 m 64-MR 10.3 MR10 Exp.2b Risk NA
## 247 NA 0 m 65-MR 9.9 MR10 Exp.2b Risk NA
## 248 NA 1 m 66-MR 10.1 MR10 Exp.2b Risk NA
## 249 1 1 m 10m_1 9.5 MR3 Exp.3b Risk 5
## 250 5 1 m 10m_5 10.4 MR3 Exp.3b Risk 5
## 251 9 1 m 10m_9 9.8 MR3 Exp.3b Risk 5
## 252 13 0 m 10m_13 10.1 MR3 Exp.3b Risk 5
## 253 17 0 m 10m_17 9.8 MR3 Exp.3b Risk 4
## 254 21 0 f 10m_21 10.7 MR3 Exp.3b Risk 5
## 255 25 0 f 10m_25 10.6 MR3 Exp.3b Risk 5
## 256 29 0 f 10m_29 9.9 MR3 Exp.3b Risk 5
## 257 33 0 f 10m_33 9.8 MR3 Exp.3b Risk 5
## 258 37 1 f 10m_37 10.2 MR3 Exp.3b Risk 5
## 259 2 0 m 10m_2 10.0 MR3 Exp.3b Risk 5
## 260 6 0 m 10m_6 10.7 MR3 Exp.3b Risk 4
## 261 10 0 m 10m_10 10.7 MR3 Exp.3b Risk 5
## 262 14 1 m 10m_14 10.7 MR3 Exp.3b Risk 5
## 263 22 1 f 10m_22 10.3 MR3 Exp.3b Risk 5
## 264 26 1 f 10m_26 11.1 MR3 Exp.3b Risk 5
## 265 30 1 f 10m_30 10.4 MR3 Exp.3b Risk 5
## 266 34 1 f 10m_34 9.8 MR3 Exp.3b Risk 5
## 267 38 1 f 10m_38 10.7 MR3 Exp.3b Risk 4
## 268 3 1 m 10m_3 10.4 MR3 Exp.3b Risk 5
## 269 7 1 m 10m_7 11.0 MR3 Exp.3b Risk 4
## 270 11 0 m 10m_11 10.1 MR3 Exp.3b Risk 5
## 271 15 0 m 10m_15 10.2 MR3 Exp.3b Risk 5
## 272 18 1 f 10m_18 10.2 MR3 Exp.3b Risk 5
## 273 19 1 m 10m_19 10.3 MR3 Exp.3b Risk 5
## 274 23 0 f 10m_23 10.3 MR3 Exp.3b Risk 5
## 275 27 0 f 10m_27 10.2 MR3 Exp.3b Risk 5
## 276 31 1 f 10m_31 9.9 MR3 Exp.3b Risk 4
## 277 35 0 f 10m_35 10.1 MR3 Exp.3b Risk 5
## 278 39 0 f 10m_39 10.0 MR3 Exp.3b Risk 5
## 279 4 0 m 10m_4 10.0 MR3 Exp.3b Risk 5
## 280 8 0 m 10m_8 10.5 MR3 Exp.3b Risk 5
## 281 12 1 m 10m_12 9.7 MR3 Exp.3b Risk 5
## 282 16 1 m 10m_16 9.6 MR3 Exp.3b Risk 5
## 283 20 0 m 10m_20 9.9 MR3 Exp.3b Risk 5
## 284 24 1 f 10m_24 10.5 MR3 Exp.3b Risk 5
## 285 28 1 f 10m_28 10.4 MR3 Exp.3b Risk 5
## 286 32 1 f 10m_32 10.2 MR3 Exp.3b Risk 5
## 287 36 0 f 10m_36 10.1 MR3 Exp.3b Risk 5
## 288 40 0 m 10m_40 10.8 MR3 Exp.3b Risk 5
## 289 1 1 m 10m_1 9.5 MR3 Exp.3b Risk 5
## 290 5 1 m 10m_5 10.4 MR3 Exp.3b Risk 5
## 291 9 1 m 10m_9 9.8 MR3 Exp.3b Risk 5
## 292 13 0 m 10m_13 10.1 MR3 Exp.3b Risk 5
## 293 17 0 m 10m_17 9.8 MR3 Exp.3b Risk 4
## 294 21 0 f 10m_21 10.7 MR3 Exp.3b Risk 5
## 295 25 0 f 10m_25 10.6 MR3 Exp.3b Risk 5
## 296 29 0 f 10m_29 9.9 MR3 Exp.3b Risk 5
## 297 33 0 f 10m_33 9.8 MR3 Exp.3b Risk 5
## 298 37 1 f 10m_37 10.2 MR3 Exp.3b Risk 5
## 299 2 0 m 10m_2 10.0 MR3 Exp.3b Risk 5
## 300 6 0 m 10m_6 10.7 MR3 Exp.3b Risk 4
## 301 10 0 m 10m_10 10.7 MR3 Exp.3b Risk 5
## 302 14 1 m 10m_14 10.7 MR3 Exp.3b Risk 5
## 303 22 1 f 10m_22 10.3 MR3 Exp.3b Risk 5
## 304 26 1 f 10m_26 11.1 MR3 Exp.3b Risk 5
## 305 30 1 f 10m_30 10.4 MR3 Exp.3b Risk 5
## 306 34 1 f 10m_34 9.8 MR3 Exp.3b Risk 5
## 307 38 1 f 10m_38 10.7 MR3 Exp.3b Risk 4
## 308 3 1 m 10m_3 10.4 MR3 Exp.3b Risk 5
## 309 7 1 m 10m_7 11.0 MR3 Exp.3b Risk 4
## 310 11 0 m 10m_11 10.1 MR3 Exp.3b Risk 5
## 311 15 0 m 10m_15 10.2 MR3 Exp.3b Risk 5
## 312 18 1 f 10m_18 10.2 MR3 Exp.3b Risk 5
## 313 19 1 m 10m_19 10.3 MR3 Exp.3b Risk 5
## 314 23 0 f 10m_23 10.3 MR3 Exp.3b Risk 5
## 315 27 0 f 10m_27 10.2 MR3 Exp.3b Risk 5
## 316 31 1 f 10m_31 9.9 MR3 Exp.3b Risk 4
## 317 35 0 f 10m_35 10.1 MR3 Exp.3b Risk 5
## 318 39 0 f 10m_39 10.0 MR3 Exp.3b Risk 5
## 319 4 0 m 10m_4 10.0 MR3 Exp.3b Risk 5
## 320 8 0 m 10m_8 10.5 MR3 Exp.3b Risk 5
## 321 12 1 m 10m_12 9.7 MR3 Exp.3b Risk 5
## 322 16 1 m 10m_16 9.6 MR3 Exp.3b Risk 5
## 323 20 0 m 10m_20 9.9 MR3 Exp.3b Risk 5
## 324 24 1 f 10m_24 10.5 MR3 Exp.3b Risk 5
## 325 28 1 f 10m_28 10.4 MR3 Exp.3b Risk 5
## 326 32 1 f 10m_32 10.2 MR3 Exp.3b Risk 5
## 327 36 0 f 10m_36 10.1 MR3 Exp.3b Risk 5
## 328 40 0 m 10m_40 10.8 MR3 Exp.3b Risk 5
## 329 1 1 m 10m_1 9.5 MR3 Exp.3b Risk 5
## 330 5 1 m 10m_5 10.4 MR3 Exp.3b Risk 5
## 331 9 1 m 10m_9 9.8 MR3 Exp.3b Risk 5
## 332 13 0 m 10m_13 10.1 MR3 Exp.3b Risk 5
## 333 17 0 m 10m_17 9.8 MR3 Exp.3b Risk 4
## 334 21 0 f 10m_21 10.7 MR3 Exp.3b Risk 5
## 335 25 0 f 10m_25 10.6 MR3 Exp.3b Risk 5
## 336 29 0 f 10m_29 9.9 MR3 Exp.3b Risk 5
## 337 33 0 f 10m_33 9.8 MR3 Exp.3b Risk 5
## 338 37 1 f 10m_37 10.2 MR3 Exp.3b Risk 5
## 339 2 0 m 10m_2 10.0 MR3 Exp.3b Risk 5
## 340 6 0 m 10m_6 10.7 MR3 Exp.3b Risk 4
## 341 10 0 m 10m_10 10.7 MR3 Exp.3b Risk 5
## 342 14 1 m 10m_14 10.7 MR3 Exp.3b Risk 5
## 343 22 1 f 10m_22 10.3 MR3 Exp.3b Risk 5
## 344 26 1 f 10m_26 11.1 MR3 Exp.3b Risk 5
## 345 30 1 f 10m_30 10.4 MR3 Exp.3b Risk 5
## 346 34 1 f 10m_34 9.8 MR3 Exp.3b Risk 5
## 347 38 1 f 10m_38 10.7 MR3 Exp.3b Risk 4
## 348 3 1 m 10m_3 10.4 MR3 Exp.3b Risk 5
## 349 7 1 m 10m_7 11.0 MR3 Exp.3b Risk 4
## 350 11 0 m 10m_11 10.1 MR3 Exp.3b Risk 5
## 351 15 0 m 10m_15 10.2 MR3 Exp.3b Risk 5
## 352 18 1 f 10m_18 10.2 MR3 Exp.3b Risk 5
## 353 19 1 m 10m_19 10.3 MR3 Exp.3b Risk 5
## 354 23 0 f 10m_23 10.3 MR3 Exp.3b Risk 5
## 355 27 0 f 10m_27 10.2 MR3 Exp.3b Risk 5
## 356 31 1 f 10m_31 9.9 MR3 Exp.3b Risk 4
## 357 35 0 f 10m_35 10.1 MR3 Exp.3b Risk 5
## 358 39 0 f 10m_39 10.0 MR3 Exp.3b Risk 5
## 359 4 0 m 10m_4 10.0 MR3 Exp.3b Risk 5
## 360 8 0 m 10m_8 10.5 MR3 Exp.3b Risk 5
## 361 12 1 m 10m_12 9.7 MR3 Exp.3b Risk 5
## 362 16 1 m 10m_16 9.6 MR3 Exp.3b Risk 5
## 363 20 0 m 10m_20 9.9 MR3 Exp.3b Risk 5
## 364 24 1 f 10m_24 10.5 MR3 Exp.3b Risk 5
## 365 28 1 f 10m_28 10.4 MR3 Exp.3b Risk 5
## 366 32 1 f 10m_32 10.2 MR3 Exp.3b Risk 5
## 367 36 0 f 10m_36 10.1 MR3 Exp.3b Risk 5
## 368 40 0 m 10m_40 10.8 MR3 Exp.3b Risk 5
## 369 1 1 m 10m_1 9.5 MR3 Exp.3b Risk 5
## 370 5 1 m 10m_5 10.4 MR3 Exp.3b Risk 5
## 371 9 1 m 10m_9 9.8 MR3 Exp.3b Risk 5
## 372 13 0 m 10m_13 10.1 MR3 Exp.3b Risk 5
## 373 17 0 m 10m_17 9.8 MR3 Exp.3b Risk 4
## 374 21 0 f 10m_21 10.7 MR3 Exp.3b Risk 5
## 375 25 0 f 10m_25 10.6 MR3 Exp.3b Risk 5
## 376 29 0 f 10m_29 9.9 MR3 Exp.3b Risk 5
## 377 33 0 f 10m_33 9.8 MR3 Exp.3b Risk 5
## 378 37 1 f 10m_37 10.2 MR3 Exp.3b Risk 5
## 379 2 0 m 10m_2 10.0 MR3 Exp.3b Risk 5
## 380 6 0 m 10m_6 10.7 MR3 Exp.3b Risk 4
## 381 10 0 m 10m_10 10.7 MR3 Exp.3b Risk 5
## 382 14 1 m 10m_14 10.7 MR3 Exp.3b Risk 5
## 383 22 1 f 10m_22 10.3 MR3 Exp.3b Risk 5
## 384 26 1 f 10m_26 11.1 MR3 Exp.3b Risk 5
## 385 30 1 f 10m_30 10.4 MR3 Exp.3b Risk 5
## 386 34 1 f 10m_34 9.8 MR3 Exp.3b Risk 5
## 387 38 1 f 10m_38 10.7 MR3 Exp.3b Risk 4
## 388 3 1 m 10m_3 10.4 MR3 Exp.3b Risk 5
## 389 7 1 m 10m_7 11.0 MR3 Exp.3b Risk 4
## 390 11 0 m 10m_11 10.1 MR3 Exp.3b Risk 5
## 391 15 0 m 10m_15 10.2 MR3 Exp.3b Risk 5
## 392 18 1 f 10m_18 10.2 MR3 Exp.3b Risk 5
## 393 19 1 m 10m_19 10.3 MR3 Exp.3b Risk 5
## 394 23 0 f 10m_23 10.3 MR3 Exp.3b Risk 5
## 395 27 0 f 10m_27 10.2 MR3 Exp.3b Risk 5
## 396 31 1 f 10m_31 9.9 MR3 Exp.3b Risk 4
## 397 35 0 f 10m_35 10.1 MR3 Exp.3b Risk 5
## 398 39 0 f 10m_39 10.0 MR3 Exp.3b Risk 5
## 399 4 0 m 10m_4 10.0 MR3 Exp.3b Risk 5
## 400 8 0 m 10m_8 10.5 MR3 Exp.3b Risk 5
## 401 12 1 m 10m_12 9.7 MR3 Exp.3b Risk 5
## 402 16 1 m 10m_16 9.6 MR3 Exp.3b Risk 5
## 403 20 0 m 10m_20 9.9 MR3 Exp.3b Risk 5
## 404 24 1 f 10m_24 10.5 MR3 Exp.3b Risk 5
## 405 28 1 f 10m_28 10.4 MR3 Exp.3b Risk 5
## 406 32 1 f 10m_32 10.2 MR3 Exp.3b Risk 5
## 407 36 0 f 10m_36 10.1 MR3 Exp.3b Risk 5
## 408 40 0 m 10m_40 10.8 MR3 Exp.3b Risk 5
## audio_quality device highchair HV_side first_test first_fam
## 1 NA NA right LV LH
## 2 NA NA left LV HL
## 3 NA NA left HV LH
## 4 NA NA right LV HL
## 5 NA NA right HV LH
## 6 NA NA left LV HL
## 7 NA NA right LV LH
## 8 NA NA right LV LH
## 9 NA NA right HV HL
## 10 NA NA left LV LH
## 11 NA NA left LV LH
## 12 NA NA right LV HL
## 13 NA NA right HV LH
## 14 NA NA left HV LH
## 15 NA NA left LV HL
## 16 NA NA right LV LH
## 17 NA NA right HV HL
## 18 NA NA left HV HL
## 19 NA NA left HV HL
## 20 NA NA right HV HL
## 21 NA NA right LV HL
## 22 NA NA right HV LH
## 23 NA NA left LV LH
## 24 NA NA left HV LH
## 25 NA NA right HV LH
## 26 NA NA left HV HL
## 27 NA NA left LV HL
## 28 NA NA left HV LH
## 29 NA NA left HV HL
## 30 NA NA right LV HL
## 31 NA NA left LV LH
## 32 NA NA right HV HL
## 33 NA NA right LV LH
## 34 NA NA left LV HL
## 35 NA NA left HV LH
## 36 NA NA right LV HL
## 37 NA NA right HV LH
## 38 NA NA left LV HL
## 39 NA NA right LV LH
## 40 NA NA right LV LH
## 41 NA NA right HV HL
## 42 NA NA left LV LH
## 43 NA NA left LV LH
## 44 NA NA right LV HL
## 45 NA NA right HV LH
## 46 NA NA left HV LH
## 47 NA NA left LV HL
## 48 NA NA right LV LH
## 49 NA NA right HV HL
## 50 NA NA left HV HL
## 51 NA NA left HV HL
## 52 NA NA right HV HL
## 53 NA NA right LV HL
## 54 NA NA right HV LH
## 55 NA NA left LV LH
## 56 NA NA left HV LH
## 57 NA NA right HV LH
## 58 NA NA left HV HL
## 59 NA NA left LV HL
## 60 NA NA left HV LH
## 61 NA NA left HV HL
## 62 NA NA right LV HL
## 63 NA NA left LV LH
## 64 NA NA right HV HL
## 65 NA NA right LV LH
## 66 NA NA left LV HL
## 67 NA NA left HV LH
## 68 NA NA right LV HL
## 69 NA NA right HV LH
## 70 NA NA left LV HL
## 71 NA NA right LV LH
## 72 NA NA right LV LH
## 73 NA NA right HV HL
## 74 NA NA left LV LH
## 75 NA NA left LV LH
## 76 NA NA right LV HL
## 77 NA NA right HV LH
## 78 NA NA left HV LH
## 79 NA NA left LV HL
## 80 NA NA right LV LH
## 81 NA NA right HV HL
## 82 NA NA left HV HL
## 83 NA NA left HV HL
## 84 NA NA right HV HL
## 85 NA NA right LV HL
## 86 NA NA right HV LH
## 87 NA NA left LV LH
## 88 NA NA left HV LH
## 89 NA NA right HV LH
## 90 NA NA left HV HL
## 91 NA NA left LV HL
## 92 NA NA left HV LH
## 93 NA NA left HV HL
## 94 NA NA right LV HL
## 95 NA NA left LV LH
## 96 NA NA right HV HL
## 97 NA NA right LV LH
## 98 NA NA left LV HL
## 99 NA NA left HV LH
## 100 NA NA right LV HL
## 101 NA NA right HV LH
## 102 NA NA left LV HL
## 103 NA NA right LV LH
## 104 NA NA right LV LH
## 105 NA NA right HV HL
## 106 NA NA left LV LH
## 107 NA NA left LV LH
## 108 NA NA right LV HL
## 109 NA NA right HV LH
## 110 NA NA left HV LH
## 111 NA NA left LV HL
## 112 NA NA right LV LH
## 113 NA NA right HV HL
## 114 NA NA left HV HL
## 115 NA NA left HV HL
## 116 NA NA right HV HL
## 117 NA NA right LV HL
## 118 NA NA right HV LH
## 119 NA NA left LV LH
## 120 NA NA left HV LH
## 121 NA NA right HV LH
## 122 NA NA left HV HL
## 123 NA NA left LV HL
## 124 NA NA left HV LH
## 125 NA NA left HV HL
## 126 NA NA right LV HL
## 127 NA NA left LV LH
## 128 NA NA right HV HL
## 129 NA NA shallow fam1
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## 249 5.0 ipad 0 A deep fam1
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## 392 5.0 laptop 0 C shallow fam1
## 393 5.0 laptop 0 C shallow fam1
## 394 5.0 laptop 0 C shallow fam1
## 395 5.0 laptop 0 C shallow fam1
## 396 4.0 laptop 0 C shallow fam1
## 397 5.0 laptop 0 C shallow fam1
## 398 5.0 laptop 0 C shallow fam1
## 399 5.0 ipad 0 D deep fam2
## 400 5.0 laptop 1 D deep fam2
## 401 4.0 laptop 0 D deep fam2
## 402 5.0 laptop 1 D deep fam2
## 403 5.0 laptop 1 D deep fam2
## 404 5.0 ipad 0 D deep fam2
## 405 4.0 laptop 0 D deep fam2
## 406 5.0 laptop 0 D deep fam2
## 407 5.0 ipad 0 D deep fam2
## 408 4.5 laptop 0 D deep fam2
## first_test_deeper_side control_deeper_side control_firstevent control_1
## 1
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16
## 17
## 18
## 19
## 20
## 21
## 22
## 23
## 24
## 25
## 26
## 27
## 28
## 29
## 30
## 31
## 32
## 33
## 34
## 35
## 36
## 37
## 38
## 39
## 40
## 41
## 42
## 43
## 44
## 45
## 46
## 47
## 48
## 49
## 50
## 51
## 52
## 53
## 54
## 55
## 56
## 57
## 58
## 59
## 60
## 61
## 62
## 63
## 64
## 65
## 66
## 67
## 68
## 69
## 70
## 71
## 72
## 73
## 74
## 75
## 76
## 77
## 78
## 79
## 80
## 81
## 82
## 83
## 84
## 85
## 86
## 87
## 88
## 89
## 90
## 91
## 92
## 93
## 94
## 95
## 96
## 97
## 98
## 99
## 100
## 101
## 102
## 103
## 104
## 105
## 106
## 107
## 108
## 109
## 110
## 111
## 112
## 113
## 114
## 115
## 116
## 117
## 118
## 119
## 120
## 121
## 122
## 123
## 124
## 125
## 126
## 127
## 128
## 129 right left deep 26.15
## 130 left left deep 4.59
## 131 left right shallow 14.55
## 132 left left deep 5.37
## 133 right right shallow 14.99
## 134 right left deep 10.2
## 135 right left deep 9.76
## 136 right left deep 16.97
## 137 left right shallow 22
## 138 right left deep 10
## 139 left right shallow 9.62
## 140 left left deep 13.23
## 141 left right shallow 19.28
## 142 right right shallow 13.02
## 143 left left deep 10.47
## 144 left right shallow 11.29
## 145 left left deep 5.85
## 146 right right shallow 9.35
## 147 right right shallow 19.89
## 148 right right shallow 26.01
## 149 left left deep 17.65
## 150 left right shallow 11.9
## 151 right left deep 16.32
## 152 right left deep 5.88
## 153 right left deep 19.21
## 154 left right shallow 10.95
## 155 right right shallow 13.53
## 156 left left deep 16.12
## 157 left left deep 22.88
## 158 right right shallow 4.56
## 159 right left deep 26.15
## 160 left left deep 4.59
## 161 left right shallow 14.55
## 162 left left deep 5.37
## 163 right right shallow 14.99
## 164 right left deep 10.2
## 165 right left deep 9.76
## 166 right left deep 16.97
## 167 left right shallow 22
## 168 right left deep 10
## 169 left right shallow 9.62
## 170 left left deep 13.23
## 171 left right shallow 19.28
## 172 right right shallow 13.02
## 173 left left deep 10.47
## 174 left right shallow 11.29
## 175 left left deep 5.85
## 176 right right shallow 9.35
## 177 right right shallow 19.89
## 178 right right shallow 26.01
## 179 left left deep 17.65
## 180 left right shallow 11.9
## 181 right left deep 16.32
## 182 right left deep 5.88
## 183 right left deep 19.21
## 184 left right shallow 10.95
## 185 right right shallow 13.53
## 186 left left deep 16.12
## 187 left left deep 22.88
## 188 right right shallow 4.56
## 189 right left deep 26.15
## 190 left left deep 4.59
## 191 left right shallow 14.55
## 192 left left deep 5.37
## 193 right right shallow 14.99
## 194 right left deep 10.2
## 195 right left deep 9.76
## 196 right left deep 16.97
## 197 left right shallow 22
## 198 right left deep 10
## 199 left right shallow 9.62
## 200 left left deep 13.23
## 201 left right shallow 19.28
## 202 right right shallow 13.02
## 203 left left deep 10.47
## 204 left right shallow 11.29
## 205 left left deep 5.85
## 206 right right shallow 9.35
## 207 right right shallow 19.89
## 208 right right shallow 26.01
## 209 left left deep 17.65
## 210 left right shallow 11.9
## 211 right left deep 16.32
## 212 right left deep 5.88
## 213 right left deep 19.21
## 214 left right shallow 10.95
## 215 right right shallow 13.53
## 216 left left deep 16.12
## 217 left left deep 22.88
## 218 right right shallow 4.56
## 219 right left deep 26.15
## 220 left left deep 4.59
## 221 left right shallow 14.55
## 222 left left deep 5.37
## 223 right right shallow 14.99
## 224 right left deep 10.2
## 225 right left deep 9.76
## 226 right left deep 16.97
## 227 left right shallow 22
## 228 right left deep 10
## 229 left right shallow 9.62
## 230 left left deep 13.23
## 231 left right shallow 19.28
## 232 right right shallow 13.02
## 233 left left deep 10.47
## 234 left right shallow 11.29
## 235 left left deep 5.85
## 236 right right shallow 9.35
## 237 right right shallow 19.89
## 238 right right shallow 26.01
## 239 left left deep 17.65
## 240 left right shallow 11.9
## 241 right left deep 16.32
## 242 right left deep 5.88
## 243 right left deep 19.21
## 244 left right shallow 10.95
## 245 right right shallow 13.53
## 246 left left deep 16.12
## 247 left left deep 22.88
## 248 right right shallow 4.56
## 249 right right deep 9.548
## 250 right right deep 4.96
## 251 right right deep 13.02
## 252 right right deep 20.088
## 253 right right deep 11.656
## 254 right right deep 31.372
## 255 right right deep 8.37
## 256 right right deep 14.074
## 257 right right deep 4.03
## 258 right right deep 20.328
## 259 right right shallow 21.824
## 260 right right shallow 26.35
## 261 right right shallow 12.09
## 262 right right shallow 7.13
## 263 right right shallow 16.058
## 264 right right shallow 12.772
## 265 right right shallow NA_13.578
## 266 right right shallow 23.002
## 267 right right shallow 10.292
## 268 left left shallow 9.796
## 269 left left shallow 6.262
## 270 left left shallow 6.572
## 271 left left shallow 11.284
## 272 left left shallow 23.064
## 273 left left shallow 11.583
## 274 left left shallow 21.948
## 275 left left shallow 17.98
## 276 left left shallow 9.92
## 277 left left shallow 12.4
## 278 left left shallow 19.107
## 279 left left deep 24.366
## 280 left left deep 14.446
## 281 left left deep 9.672
## 282 left left deep 17.112
## 283 left left deep 6.882
## 284 left left deep NA_27.28
## 285 left left deep NA_10.044
## 286 left left deep 11.844
## 287 left left deep 10.912
## 288 left left deep 15.004
## 289 right right deep 9.548
## 290 right right deep 4.96
## 291 right right deep 13.02
## 292 right right deep 20.088
## 293 right right deep 11.656
## 294 right right deep 31.372
## 295 right right deep 8.37
## 296 right right deep 14.074
## 297 right right deep 4.03
## 298 right right deep 20.328
## 299 right right shallow 21.824
## 300 right right shallow 26.35
## 301 right right shallow 12.09
## 302 right right shallow 7.13
## 303 right right shallow 16.058
## 304 right right shallow 12.772
## 305 right right shallow NA_13.578
## 306 right right shallow 23.002
## 307 right right shallow 10.292
## 308 left left shallow 9.796
## 309 left left shallow 6.262
## 310 left left shallow 6.572
## 311 left left shallow 11.284
## 312 left left shallow 23.064
## 313 left left shallow 11.583
## 314 left left shallow 21.948
## 315 left left shallow 17.98
## 316 left left shallow 9.92
## 317 left left shallow 12.4
## 318 left left shallow 19.107
## 319 left left deep 24.366
## 320 left left deep 14.446
## 321 left left deep 9.672
## 322 left left deep 17.112
## 323 left left deep 6.882
## 324 left left deep NA_27.28
## 325 left left deep NA_10.044
## 326 left left deep 11.844
## 327 left left deep 10.912
## 328 left left deep 15.004
## 329 right right deep 9.548
## 330 right right deep 4.96
## 331 right right deep 13.02
## 332 right right deep 20.088
## 333 right right deep 11.656
## 334 right right deep 31.372
## 335 right right deep 8.37
## 336 right right deep 14.074
## 337 right right deep 4.03
## 338 right right deep 20.328
## 339 right right shallow 21.824
## 340 right right shallow 26.35
## 341 right right shallow 12.09
## 342 right right shallow 7.13
## 343 right right shallow 16.058
## 344 right right shallow 12.772
## 345 right right shallow NA_13.578
## 346 right right shallow 23.002
## 347 right right shallow 10.292
## 348 left left shallow 9.796
## 349 left left shallow 6.262
## 350 left left shallow 6.572
## 351 left left shallow 11.284
## 352 left left shallow 23.064
## 353 left left shallow 11.583
## 354 left left shallow 21.948
## 355 left left shallow 17.98
## 356 left left shallow 9.92
## 357 left left shallow 12.4
## 358 left left shallow 19.107
## 359 left left deep 24.366
## 360 left left deep 14.446
## 361 left left deep 9.672
## 362 left left deep 17.112
## 363 left left deep 6.882
## 364 left left deep NA_27.28
## 365 left left deep NA_10.044
## 366 left left deep 11.844
## 367 left left deep 10.912
## 368 left left deep 15.004
## 369 right right deep 9.548
## 370 right right deep 4.96
## 371 right right deep 13.02
## 372 right right deep 20.088
## 373 right right deep 11.656
## 374 right right deep 31.372
## 375 right right deep 8.37
## 376 right right deep 14.074
## 377 right right deep 4.03
## 378 right right deep 20.328
## 379 right right shallow 21.824
## 380 right right shallow 26.35
## 381 right right shallow 12.09
## 382 right right shallow 7.13
## 383 right right shallow 16.058
## 384 right right shallow 12.772
## 385 right right shallow NA_13.578
## 386 right right shallow 23.002
## 387 right right shallow 10.292
## 388 left left shallow 9.796
## 389 left left shallow 6.262
## 390 left left shallow 6.572
## 391 left left shallow 11.284
## 392 left left shallow 23.064
## 393 left left shallow 11.583
## 394 left left shallow 21.948
## 395 left left shallow 17.98
## 396 left left shallow 9.92
## 397 left left shallow 12.4
## 398 left left shallow 19.107
## 399 left left deep 24.366
## 400 left left deep 14.446
## 401 left left deep 9.672
## 402 left left deep 17.112
## 403 left left deep 6.882
## 404 left left deep NA_27.28
## 405 left left deep NA_10.044
## 406 left left deep 11.844
## 407 left left deep 10.912
## 408 left left deep 15.004
## control_2 fam1 fam2 fam3 fam4 fam5 fam6
## 1 60 60 21.3 3.6 5.5 2.7
## 2 60 53.2 33.2 5.1 8.5 4.8
## 3 60 60 36.5 40.7 30.6 13.4
## 4 60 60 44.9 11.3 23.9 16.6
## 5 60 5.6 60 48.7 2.9 3.9
## 6 60 39.9 15.5 4.8 10.8 7
## 7 60 42.5 60 42.1 21.7 22.7
## 8 34.9 15.2 4.6 3.1 4.3 6.6
## 9 60 12.3 10.8 32.5 58.7 13.9
## 10 60 60 60 60 14.6 27.8
## 11 60 60 45.8 19.9 20.4 25.7
## 12 23.9 27.2 57 12.9 4.8 60
## 13 60 24.7 31.4 21.7 19.5 25.6
## 14 33.6 60 14.8 23.3 14.3 6.4
## 15 31.2 6.3 6.3 5.6 3.7 12.8
## 16 23.4 8.3 12.2 8.5 3.1 3
## 17 60 60 53.2 58.5 6.9 3.5
## 18 60 60 6 14.3 27.2 4.7
## 19 60 60 60 34.3 21.8 10.3
## 20 60 60 29.2 60 48.4 4
## 21 60 60 35.2 17.9 10.8 21.1
## 22 60 60 60 27.5 12.4 9.9
## 23 60 25.3 49 12.1 17.7 10.5
## 24 47.1 60 25.2 12.3 3.5 8.9
## 25 60 60 60 11.3 21.1 10.4
## 26 60 60 39.6 32.1 5.1 4.7
## 27 60 52.8 60 50.1 21.4 14.2
## 28 <NA> <NA> <NA> <NA> <NA> <NA>
## 29 28.99 20.57 46.76 37.78 40.04 6.85
## 30 59.6 19.4 10.8 4.2 3.7 3.9
## 31 60 60 23.2 42.9 14 22.6
## 32 60 60 60 60 60 60
## 33 60 60 21.3 3.6 5.5 2.7
## 34 60 53.2 33.2 5.1 8.5 4.8
## 35 60 60 36.5 40.7 30.6 13.4
## 36 60 60 44.9 11.3 23.9 16.6
## 37 60 5.6 60 48.7 2.9 3.9
## 38 60 39.9 15.5 4.8 10.8 7
## 39 60 42.5 60 42.1 21.7 22.7
## 40 34.9 15.2 4.6 3.1 4.3 6.6
## 41 60 12.3 10.8 32.5 58.7 13.9
## 42 60 60 60 60 14.6 27.8
## 43 60 60 45.8 19.9 20.4 25.7
## 44 23.9 27.2 57 12.9 4.8 60
## 45 60 24.7 31.4 21.7 19.5 25.6
## 46 33.6 60 14.8 23.3 14.3 6.4
## 47 31.2 6.3 6.3 5.6 3.7 12.8
## 48 23.4 8.3 12.2 8.5 3.1 3
## 49 60 60 53.2 58.5 6.9 3.5
## 50 60 60 6 14.3 27.2 4.7
## 51 60 60 60 34.3 21.8 10.3
## 52 60 60 29.2 60 48.4 4
## 53 60 60 35.2 17.9 10.8 21.1
## 54 60 60 60 27.5 12.4 9.9
## 55 60 25.3 49 12.1 17.7 10.5
## 56 47.1 60 25.2 12.3 3.5 8.9
## 57 60 60 60 11.3 21.1 10.4
## 58 60 60 39.6 32.1 5.1 4.7
## 59 60 52.8 60 50.1 21.4 14.2
## 60 <NA> <NA> <NA> <NA> <NA> <NA>
## 61 28.99 20.57 46.76 37.78 40.04 6.85
## 62 59.6 19.4 10.8 4.2 3.7 3.9
## 63 60 60 23.2 42.9 14 22.6
## 64 60 60 60 60 60 60
## 65 60 60 21.3 3.6 5.5 2.7
## 66 60 53.2 33.2 5.1 8.5 4.8
## 67 60 60 36.5 40.7 30.6 13.4
## 68 60 60 44.9 11.3 23.9 16.6
## 69 60 5.6 60 48.7 2.9 3.9
## 70 60 39.9 15.5 4.8 10.8 7
## 71 60 42.5 60 42.1 21.7 22.7
## 72 34.9 15.2 4.6 3.1 4.3 6.6
## 73 60 12.3 10.8 32.5 58.7 13.9
## 74 60 60 60 60 14.6 27.8
## 75 60 60 45.8 19.9 20.4 25.7
## 76 23.9 27.2 57 12.9 4.8 60
## 77 60 24.7 31.4 21.7 19.5 25.6
## 78 33.6 60 14.8 23.3 14.3 6.4
## 79 31.2 6.3 6.3 5.6 3.7 12.8
## 80 23.4 8.3 12.2 8.5 3.1 3
## 81 60 60 53.2 58.5 6.9 3.5
## 82 60 60 6 14.3 27.2 4.7
## 83 60 60 60 34.3 21.8 10.3
## 84 60 60 29.2 60 48.4 4
## 85 60 60 35.2 17.9 10.8 21.1
## 86 60 60 60 27.5 12.4 9.9
## 87 60 25.3 49 12.1 17.7 10.5
## 88 47.1 60 25.2 12.3 3.5 8.9
## 89 60 60 60 11.3 21.1 10.4
## 90 60 60 39.6 32.1 5.1 4.7
## 91 60 52.8 60 50.1 21.4 14.2
## 92 <NA> <NA> <NA> <NA> <NA> <NA>
## 93 28.99 20.57 46.76 37.78 40.04 6.85
## 94 59.6 19.4 10.8 4.2 3.7 3.9
## 95 60 60 23.2 42.9 14 22.6
## 96 60 60 60 60 60 60
## 97 60 60 21.3 3.6 5.5 2.7
## 98 60 53.2 33.2 5.1 8.5 4.8
## 99 60 60 36.5 40.7 30.6 13.4
## 100 60 60 44.9 11.3 23.9 16.6
## 101 60 5.6 60 48.7 2.9 3.9
## 102 60 39.9 15.5 4.8 10.8 7
## 103 60 42.5 60 42.1 21.7 22.7
## 104 34.9 15.2 4.6 3.1 4.3 6.6
## 105 60 12.3 10.8 32.5 58.7 13.9
## 106 60 60 60 60 14.6 27.8
## 107 60 60 45.8 19.9 20.4 25.7
## 108 23.9 27.2 57 12.9 4.8 60
## 109 60 24.7 31.4 21.7 19.5 25.6
## 110 33.6 60 14.8 23.3 14.3 6.4
## 111 31.2 6.3 6.3 5.6 3.7 12.8
## 112 23.4 8.3 12.2 8.5 3.1 3
## 113 60 60 53.2 58.5 6.9 3.5
## 114 60 60 6 14.3 27.2 4.7
## 115 60 60 60 34.3 21.8 10.3
## 116 60 60 29.2 60 48.4 4
## 117 60 60 35.2 17.9 10.8 21.1
## 118 60 60 60 27.5 12.4 9.9
## 119 60 25.3 49 12.1 17.7 10.5
## 120 47.1 60 25.2 12.3 3.5 8.9
## 121 60 60 60 11.3 21.1 10.4
## 122 60 60 39.6 32.1 5.1 4.7
## 123 60 52.8 60 50.1 21.4 14.2
## 124 <NA> <NA> <NA> <NA> <NA> <NA>
## 125 28.99 20.57 46.76 37.78 40.04 6.85
## 126 59.6 19.4 10.8 4.2 3.7 3.9
## 127 60 60 23.2 42.9 14 22.6
## 128 60 60 60 60 60 60
## 129 20.77 58.7 60 60 23.6 60 34
## 130 6.36 60 60 18 11.7 60 60
## 131 20.77 60 60 23.1 5.2 23.8 13.8
## 132 5.68 60 60 60 60 60 60
## 133 11.02 60 60 60 52.5 6.4 3
## 134 18.19 14.3 11.7 5.9 6.4 6.9 7.1
## 135 8.4 60 23.7 19.1 19.5 4.4 7.3
## 136 53.69 60 60 60 60 60 23
## 137 3.2 60 60 60 60 33.8 28.2
## 138 23.36 60 60 60 21.3 10.3 3.2
## 139 39.27 60 60 19.3 60 40.3 7.4
## 140 14.55 48.9 31.8 4.4 4.1 11.8 8.1
## 141 8.23 60 35.1 11.4 31.5 3.3 2.8
## 142 12.58 14.2 24.8 60 60 59 60
## 143 5.76 60 44.9 40 17.1 3.8 3.3
## 144 5.78 60 60 55.2 32.2 19.4 8.6
## 145 4.39 21.8 13.5 11.9 7 4.4 28.5
## 146 7.62 43.2 60 21.1 22.2 29.9 6.6
## 147 12 60 60 26.5 21.2 31 11.7
## 148 14.25 60 49.9 60 40.8 57.8 14.2
## 149 6.83 60 16.2 46.4 57 22.7 5.5
## 150 6.94 60 24.4 4.9 23.3 23.9 5.4
## 151 10.3 60 60 12 16.1 25.9 5.5
## 152 7.31 60 60 58.4 21 21.2 25.8
## 153 4.76 60 40.7 13.6 4.7 5.1 19.7
## 154 19.11 60 60 39.5 12.4 11.6 10.8
## 155 6.05 60 58.6 60 51.1 7 32.3
## 156 27.34 60 60 47.4 60 13.3 13.1
## 157 21.62 60 58.1 60 19.2 12.2 10.4
## 158 4.69 60 60 11.2 20.2 20.2 48.7
## 159 20.77 58.7 60 60 23.6 60 34
## 160 6.36 60 60 18 11.7 60 60
## 161 20.77 60 60 23.1 5.2 23.8 13.8
## 162 5.68 60 60 60 60 60 60
## 163 11.02 60 60 60 52.5 6.4 3
## 164 18.19 14.3 11.7 5.9 6.4 6.9 7.1
## 165 8.4 60 23.7 19.1 19.5 4.4 7.3
## 166 53.69 60 60 60 60 60 23
## 167 3.2 60 60 60 60 33.8 28.2
## 168 23.36 60 60 60 21.3 10.3 3.2
## 169 39.27 60 60 19.3 60 40.3 7.4
## 170 14.55 48.9 31.8 4.4 4.1 11.8 8.1
## 171 8.23 60 35.1 11.4 31.5 3.3 2.8
## 172 12.58 14.2 24.8 60 60 59 60
## 173 5.76 60 44.9 40 17.1 3.8 3.3
## 174 5.78 60 60 55.2 32.2 19.4 8.6
## 175 4.39 21.8 13.5 11.9 7 4.4 28.5
## 176 7.62 43.2 60 21.1 22.2 29.9 6.6
## 177 12 60 60 26.5 21.2 31 11.7
## 178 14.25 60 49.9 60 40.8 57.8 14.2
## 179 6.83 60 16.2 46.4 57 22.7 5.5
## 180 6.94 60 24.4 4.9 23.3 23.9 5.4
## 181 10.3 60 60 12 16.1 25.9 5.5
## 182 7.31 60 60 58.4 21 21.2 25.8
## 183 4.76 60 40.7 13.6 4.7 5.1 19.7
## 184 19.11 60 60 39.5 12.4 11.6 10.8
## 185 6.05 60 58.6 60 51.1 7 32.3
## 186 27.34 60 60 47.4 60 13.3 13.1
## 187 21.62 60 58.1 60 19.2 12.2 10.4
## 188 4.69 60 60 11.2 20.2 20.2 48.7
## 189 20.77 58.7 60 60 23.6 60 34
## 190 6.36 60 60 18 11.7 60 60
## 191 20.77 60 60 23.1 5.2 23.8 13.8
## 192 5.68 60 60 60 60 60 60
## 193 11.02 60 60 60 52.5 6.4 3
## 194 18.19 14.3 11.7 5.9 6.4 6.9 7.1
## 195 8.4 60 23.7 19.1 19.5 4.4 7.3
## 196 53.69 60 60 60 60 60 23
## 197 3.2 60 60 60 60 33.8 28.2
## 198 23.36 60 60 60 21.3 10.3 3.2
## 199 39.27 60 60 19.3 60 40.3 7.4
## 200 14.55 48.9 31.8 4.4 4.1 11.8 8.1
## 201 8.23 60 35.1 11.4 31.5 3.3 2.8
## 202 12.58 14.2 24.8 60 60 59 60
## 203 5.76 60 44.9 40 17.1 3.8 3.3
## 204 5.78 60 60 55.2 32.2 19.4 8.6
## 205 4.39 21.8 13.5 11.9 7 4.4 28.5
## 206 7.62 43.2 60 21.1 22.2 29.9 6.6
## 207 12 60 60 26.5 21.2 31 11.7
## 208 14.25 60 49.9 60 40.8 57.8 14.2
## 209 6.83 60 16.2 46.4 57 22.7 5.5
## 210 6.94 60 24.4 4.9 23.3 23.9 5.4
## 211 10.3 60 60 12 16.1 25.9 5.5
## 212 7.31 60 60 58.4 21 21.2 25.8
## 213 4.76 60 40.7 13.6 4.7 5.1 19.7
## 214 19.11 60 60 39.5 12.4 11.6 10.8
## 215 6.05 60 58.6 60 51.1 7 32.3
## 216 27.34 60 60 47.4 60 13.3 13.1
## 217 21.62 60 58.1 60 19.2 12.2 10.4
## 218 4.69 60 60 11.2 20.2 20.2 48.7
## 219 20.77 58.7 60 60 23.6 60 34
## 220 6.36 60 60 18 11.7 60 60
## 221 20.77 60 60 23.1 5.2 23.8 13.8
## 222 5.68 60 60 60 60 60 60
## 223 11.02 60 60 60 52.5 6.4 3
## 224 18.19 14.3 11.7 5.9 6.4 6.9 7.1
## 225 8.4 60 23.7 19.1 19.5 4.4 7.3
## 226 53.69 60 60 60 60 60 23
## 227 3.2 60 60 60 60 33.8 28.2
## 228 23.36 60 60 60 21.3 10.3 3.2
## 229 39.27 60 60 19.3 60 40.3 7.4
## 230 14.55 48.9 31.8 4.4 4.1 11.8 8.1
## 231 8.23 60 35.1 11.4 31.5 3.3 2.8
## 232 12.58 14.2 24.8 60 60 59 60
## 233 5.76 60 44.9 40 17.1 3.8 3.3
## 234 5.78 60 60 55.2 32.2 19.4 8.6
## 235 4.39 21.8 13.5 11.9 7 4.4 28.5
## 236 7.62 43.2 60 21.1 22.2 29.9 6.6
## 237 12 60 60 26.5 21.2 31 11.7
## 238 14.25 60 49.9 60 40.8 57.8 14.2
## 239 6.83 60 16.2 46.4 57 22.7 5.5
## 240 6.94 60 24.4 4.9 23.3 23.9 5.4
## 241 10.3 60 60 12 16.1 25.9 5.5
## 242 7.31 60 60 58.4 21 21.2 25.8
## 243 4.76 60 40.7 13.6 4.7 5.1 19.7
## 244 19.11 60 60 39.5 12.4 11.6 10.8
## 245 6.05 60 58.6 60 51.1 7 32.3
## 246 27.34 60 60 47.4 60 13.3 13.1
## 247 21.62 60 58.1 60 19.2 12.2 10.4
## 248 4.69 60 60 11.2 20.2 20.2 48.7
## 249 6.173 60 56.094 17.05 49.166 NA_5.89 7.812
## 250 8.618 60 60 57.334 59.008 39.618 NA_17.422
## 251 5.952 60 60 60 59.194 NA_27.59 23.188
## 252 17.36 60 60 53.986 59.628 59.566 60
## 253 10.664 58.016 58.698 28.83 2.17 57.334 4.216
## 254 9.238 18.352 NA_2.108 12.4 7.998 25.668 9.114
## 255 8.618 55.164 NA_0.496 54.358 39.928 25.296 NA_4.65
## 256 8.308 29.202 28.644 18.786 19.84 45.198 11.408
## 257 4.03 14.384 13.95 31.868 NA_4.588 NA_4.712 13.764
## 258 4.752 59.142 NA_3.861 33.363 13.563 NA_3.201 29.37
## 259 6.262 56.652 60 60 28.024 15.376 22.692
## 260 9.672 58.946 60 57.21 31.806 30.69 14.818
## 261 6.882 NA_24.738 34.162 35.03 12.214 55.412 14.384
## 262 9.362 60 60 60 60 58.76 24.8
## 263 7.626 60 60 24.49 NA_6.758 6.882 44.33
## 264 37.696 60 60 58.326 NA_45.322 27.032 33.232
## 265 NA_8.742 34.162 45.198 NA_4.154 NA_5.456 NA_4.588 10.726
## 266 21.452 60 60 60 8.99 16.616 12.71
## 267 4.96 55.428 54.916 40.486 28.086 29.698 9.362
## 268 5.642 28.458 6.634 16.492 13.95 20.584 12.958
## 269 6.634 27.156 10.726 6.572 6.696 8.804 8.556
## 270 5.332 30.566 40.734 NA_4.34 12.524 NA_5.456 13.268
## 271 10.912 60 58.946 57.396 39.742 7.378 NA_4.836
## 272 10.602 60 25.11 14.508 39.184 15.872 8.308
## 273 4.836 57.226 60 60 60 38.068 14.632
## 274 9.362 60 60 60 60 60 60
## 275 7.688 59.69 60 NA_3.658 57.458 35.402 51.708
## 276 10.23 60 57.458 23.746 31.186 12.524 15.252
## 277 3.596 NA_63.054 13.268 58.016 31 18.538 NA_15.19
## 278 39.927 60 60 58.449 59.01 56.106 37.587
## 279 8.37 NA_21.39 7.564 40.424 12.648 32.302 52.018
## 280 22.444 60 56.9 11.532 14.322 10.974 38.192
## 281 6.386 55.66 24.118 59.504 49.6 7.192 NA_7.316
## 282 15.252 28.272 58.574 33.542 12.896 26.598 NA_3.596
## 283 6.014 11.284 4.092 2.666 22.692 10.602 21.39
## 284 NA_7.936 60 58.14 31 18.972 8.928 16.802
## 285 NA_5.766 60 NA_48.98 60 14.074 12.648 21.886
## 286 5.146 57.784 NA_15.872 NA_5.146 29.326 42.904 20.832
## 287 21.39 37.2 19.406 58.636 19.158 9.486 9.114
## 288 7.006 34.41 NA_5.89 45.88 27.9 29.822 12.214
## 289 6.173 60 56.094 17.05 49.166 NA_5.89 7.812
## 290 8.618 60 60 57.334 59.008 39.618 NA_17.422
## 291 5.952 60 60 60 59.194 NA_27.59 23.188
## 292 17.36 60 60 53.986 59.628 59.566 60
## 293 10.664 58.016 58.698 28.83 2.17 57.334 4.216
## 294 9.238 18.352 NA_2.108 12.4 7.998 25.668 9.114
## 295 8.618 55.164 NA_0.496 54.358 39.928 25.296 NA_4.65
## 296 8.308 29.202 28.644 18.786 19.84 45.198 11.408
## 297 4.03 14.384 13.95 31.868 NA_4.588 NA_4.712 13.764
## 298 4.752 59.142 NA_3.861 33.363 13.563 NA_3.201 29.37
## 299 6.262 56.652 60 60 28.024 15.376 22.692
## 300 9.672 58.946 60 57.21 31.806 30.69 14.818
## 301 6.882 NA_24.738 34.162 35.03 12.214 55.412 14.384
## 302 9.362 60 60 60 60 58.76 24.8
## 303 7.626 60 60 24.49 NA_6.758 6.882 44.33
## 304 37.696 60 60 58.326 NA_45.322 27.032 33.232
## 305 NA_8.742 34.162 45.198 NA_4.154 NA_5.456 NA_4.588 10.726
## 306 21.452 60 60 60 8.99 16.616 12.71
## 307 4.96 55.428 54.916 40.486 28.086 29.698 9.362
## 308 5.642 28.458 6.634 16.492 13.95 20.584 12.958
## 309 6.634 27.156 10.726 6.572 6.696 8.804 8.556
## 310 5.332 30.566 40.734 NA_4.34 12.524 NA_5.456 13.268
## 311 10.912 60 58.946 57.396 39.742 7.378 NA_4.836
## 312 10.602 60 25.11 14.508 39.184 15.872 8.308
## 313 4.836 57.226 60 60 60 38.068 14.632
## 314 9.362 60 60 60 60 60 60
## 315 7.688 59.69 60 NA_3.658 57.458 35.402 51.708
## 316 10.23 60 57.458 23.746 31.186 12.524 15.252
## 317 3.596 NA_63.054 13.268 58.016 31 18.538 NA_15.19
## 318 39.927 60 60 58.449 59.01 56.106 37.587
## 319 8.37 NA_21.39 7.564 40.424 12.648 32.302 52.018
## 320 22.444 60 56.9 11.532 14.322 10.974 38.192
## 321 6.386 55.66 24.118 59.504 49.6 7.192 NA_7.316
## 322 15.252 28.272 58.574 33.542 12.896 26.598 NA_3.596
## 323 6.014 11.284 4.092 2.666 22.692 10.602 21.39
## 324 NA_7.936 60 58.14 31 18.972 8.928 16.802
## 325 NA_5.766 60 NA_48.98 60 14.074 12.648 21.886
## 326 5.146 57.784 NA_15.872 NA_5.146 29.326 42.904 20.832
## 327 21.39 37.2 19.406 58.636 19.158 9.486 9.114
## 328 7.006 34.41 NA_5.89 45.88 27.9 29.822 12.214
## 329 6.173 60 56.094 17.05 49.166 NA_5.89 7.812
## 330 8.618 60 60 57.334 59.008 39.618 NA_17.422
## 331 5.952 60 60 60 59.194 NA_27.59 23.188
## 332 17.36 60 60 53.986 59.628 59.566 60
## 333 10.664 58.016 58.698 28.83 2.17 57.334 4.216
## 334 9.238 18.352 NA_2.108 12.4 7.998 25.668 9.114
## 335 8.618 55.164 NA_0.496 54.358 39.928 25.296 NA_4.65
## 336 8.308 29.202 28.644 18.786 19.84 45.198 11.408
## 337 4.03 14.384 13.95 31.868 NA_4.588 NA_4.712 13.764
## 338 4.752 59.142 NA_3.861 33.363 13.563 NA_3.201 29.37
## 339 6.262 56.652 60 60 28.024 15.376 22.692
## 340 9.672 58.946 60 57.21 31.806 30.69 14.818
## 341 6.882 NA_24.738 34.162 35.03 12.214 55.412 14.384
## 342 9.362 60 60 60 60 58.76 24.8
## 343 7.626 60 60 24.49 NA_6.758 6.882 44.33
## 344 37.696 60 60 58.326 NA_45.322 27.032 33.232
## 345 NA_8.742 34.162 45.198 NA_4.154 NA_5.456 NA_4.588 10.726
## 346 21.452 60 60 60 8.99 16.616 12.71
## 347 4.96 55.428 54.916 40.486 28.086 29.698 9.362
## 348 5.642 28.458 6.634 16.492 13.95 20.584 12.958
## 349 6.634 27.156 10.726 6.572 6.696 8.804 8.556
## 350 5.332 30.566 40.734 NA_4.34 12.524 NA_5.456 13.268
## 351 10.912 60 58.946 57.396 39.742 7.378 NA_4.836
## 352 10.602 60 25.11 14.508 39.184 15.872 8.308
## 353 4.836 57.226 60 60 60 38.068 14.632
## 354 9.362 60 60 60 60 60 60
## 355 7.688 59.69 60 NA_3.658 57.458 35.402 51.708
## 356 10.23 60 57.458 23.746 31.186 12.524 15.252
## 357 3.596 NA_63.054 13.268 58.016 31 18.538 NA_15.19
## 358 39.927 60 60 58.449 59.01 56.106 37.587
## 359 8.37 NA_21.39 7.564 40.424 12.648 32.302 52.018
## 360 22.444 60 56.9 11.532 14.322 10.974 38.192
## 361 6.386 55.66 24.118 59.504 49.6 7.192 NA_7.316
## 362 15.252 28.272 58.574 33.542 12.896 26.598 NA_3.596
## 363 6.014 11.284 4.092 2.666 22.692 10.602 21.39
## 364 NA_7.936 60 58.14 31 18.972 8.928 16.802
## 365 NA_5.766 60 NA_48.98 60 14.074 12.648 21.886
## 366 5.146 57.784 NA_15.872 NA_5.146 29.326 42.904 20.832
## 367 21.39 37.2 19.406 58.636 19.158 9.486 9.114
## 368 7.006 34.41 NA_5.89 45.88 27.9 29.822 12.214
## 369 6.173 60 56.094 17.05 49.166 NA_5.89 7.812
## 370 8.618 60 60 57.334 59.008 39.618 NA_17.422
## 371 5.952 60 60 60 59.194 NA_27.59 23.188
## 372 17.36 60 60 53.986 59.628 59.566 60
## 373 10.664 58.016 58.698 28.83 2.17 57.334 4.216
## 374 9.238 18.352 NA_2.108 12.4 7.998 25.668 9.114
## 375 8.618 55.164 NA_0.496 54.358 39.928 25.296 NA_4.65
## 376 8.308 29.202 28.644 18.786 19.84 45.198 11.408
## 377 4.03 14.384 13.95 31.868 NA_4.588 NA_4.712 13.764
## 378 4.752 59.142 NA_3.861 33.363 13.563 NA_3.201 29.37
## 379 6.262 56.652 60 60 28.024 15.376 22.692
## 380 9.672 58.946 60 57.21 31.806 30.69 14.818
## 381 6.882 NA_24.738 34.162 35.03 12.214 55.412 14.384
## 382 9.362 60 60 60 60 58.76 24.8
## 383 7.626 60 60 24.49 NA_6.758 6.882 44.33
## 384 37.696 60 60 58.326 NA_45.322 27.032 33.232
## 385 NA_8.742 34.162 45.198 NA_4.154 NA_5.456 NA_4.588 10.726
## 386 21.452 60 60 60 8.99 16.616 12.71
## 387 4.96 55.428 54.916 40.486 28.086 29.698 9.362
## 388 5.642 28.458 6.634 16.492 13.95 20.584 12.958
## 389 6.634 27.156 10.726 6.572 6.696 8.804 8.556
## 390 5.332 30.566 40.734 NA_4.34 12.524 NA_5.456 13.268
## 391 10.912 60 58.946 57.396 39.742 7.378 NA_4.836
## 392 10.602 60 25.11 14.508 39.184 15.872 8.308
## 393 4.836 57.226 60 60 60 38.068 14.632
## 394 9.362 60 60 60 60 60 60
## 395 7.688 59.69 60 NA_3.658 57.458 35.402 51.708
## 396 10.23 60 57.458 23.746 31.186 12.524 15.252
## 397 3.596 NA_63.054 13.268 58.016 31 18.538 NA_15.19
## 398 39.927 60 60 58.449 59.01 56.106 37.587
## 399 8.37 NA_21.39 7.564 40.424 12.648 32.302 52.018
## 400 22.444 60 56.9 11.532 14.322 10.974 38.192
## 401 6.386 55.66 24.118 59.504 49.6 7.192 NA_7.316
## 402 15.252 28.272 58.574 33.542 12.896 26.598 NA_3.596
## 403 6.014 11.284 4.092 2.666 22.692 10.602 21.39
## 404 NA_7.936 60 58.14 31 18.972 8.928 16.802
## 405 NA_5.766 60 NA_48.98 60 14.074 12.648 21.886
## 406 5.146 57.784 NA_15.872 NA_5.146 29.326 42.904 20.832
## 407 21.39 37.2 19.406 58.636 19.158 9.486 9.114
## 408 7.006 34.41 NA_5.89 45.88 27.9 29.822 12.214
## test1 test2 test3 test4 avg_fam sum_fam phase
## 1 24.94 13.03 7.44 9.08 25.5 153 testavg
## 2 34.62 41.24 14.73 28.74 27.5 165 testavg
## 3 15.31 33.44 7.82 5.64 40.2 241 testavg
## 4 13.29 8.58 6.79 3.3 36.1 217 testavg
## 5 27.75 16.84 5.66 11.86 30.2 181 testavg
## 6 7 9.88 16.86 15.48 23.0 138 testavg
## 7 35.42 17.34 12.72 20.34 41.5 249 testavg
## 8 <NA> <NA> 23.87 11.4 11.4 69 testavg
## 9 7.58 35.76 7.67 13.08 31.4 188 testavg
## 10 45.55 8.28 13.88 13.26 47.1 282 testavg
## 11 11.97 9.93 6.78 6.73 38.6 232 testavg
## 12 37.1 24.34 7.87 44.71 31.0 186 testavg
## 13 60 29.45 35.41 9.88 30.5 183 testavg
## 14 9.06 20.53 8.58 14.72 25.4 152 testavg
## 15 14.22 8.76 15.34 9.49 11.0 66 testavg
## 16 7.16 7.32 15.01 7.26 9.8 58 testavg
## 17 37.22 24.93 16.09 6.8 40.4 242 testavg
## 18 19.04 21.22 12.79 7.87 28.7 172 testavg
## 19 60 26.16 35.51 12.48 41.1 246 testavg
## 20 25.47 8.33 6.91 6.98 43.6 262 testavg
## 21 56.8 15.89 4.07 4.19 34.2 205 testavg
## 22 16.18 20.07 <NA> <NA> 38.3 230 testavg
## 23 25.27 24.22 6.72 37.59 29.1 175 testavg
## 24 12.87 40.06 34.31 24.09 26.2 157 testavg
## 25 16.84 4.09 <NA> <NA> 37.1 223 testavg
## 26 17.99 21.39 <NA> <NA> 33.6 202 testavg
## 27 19.92 18.07 8.63 5.69 43.1 258 testavg
## 28 6.11 25.01 41.03 7.34 NA NA testavg
## 29 28.38 15.62 16.46 4.27 30.2 181 testavg
## 30 9.32 5.42 3.42 13.29 16.9 102 testavg
## 31 24.65 60 60 12.8 37.1 223 testavg
## 32 39.41 60 19.82 60 60.0 360 testavg
## 33 24.94 13.03 7.44 9.08 25.5 153 testavg
## 34 34.62 41.24 14.73 28.74 27.5 165 testavg
## 35 15.31 33.44 7.82 5.64 40.2 241 testavg
## 36 13.29 8.58 6.79 3.3 36.1 217 testavg
## 37 27.75 16.84 5.66 11.86 30.2 181 testavg
## 38 7 9.88 16.86 15.48 23.0 138 testavg
## 39 35.42 17.34 12.72 20.34 41.5 249 testavg
## 40 <NA> <NA> 23.87 11.4 11.4 69 testavg
## 41 7.58 35.76 7.67 13.08 31.4 188 testavg
## 42 45.55 8.28 13.88 13.26 47.1 282 testavg
## 43 11.97 9.93 6.78 6.73 38.6 232 testavg
## 44 37.1 24.34 7.87 44.71 31.0 186 testavg
## 45 60 29.45 35.41 9.88 30.5 183 testavg
## 46 9.06 20.53 8.58 14.72 25.4 152 testavg
## 47 14.22 8.76 15.34 9.49 11.0 66 testavg
## 48 7.16 7.32 15.01 7.26 9.8 58 testavg
## 49 37.22 24.93 16.09 6.8 40.4 242 testavg
## 50 19.04 21.22 12.79 7.87 28.7 172 testavg
## 51 60 26.16 35.51 12.48 41.1 246 testavg
## 52 25.47 8.33 6.91 6.98 43.6 262 testavg
## 53 56.8 15.89 4.07 4.19 34.2 205 testavg
## 54 16.18 20.07 <NA> <NA> 38.3 230 testavg
## 55 25.27 24.22 6.72 37.59 29.1 175 testavg
## 56 12.87 40.06 34.31 24.09 26.2 157 testavg
## 57 16.84 4.09 <NA> <NA> 37.1 223 testavg
## 58 17.99 21.39 <NA> <NA> 33.6 202 testavg
## 59 19.92 18.07 8.63 5.69 43.1 258 testavg
## 60 6.11 25.01 41.03 7.34 NA NA testavg
## 61 28.38 15.62 16.46 4.27 30.2 181 testavg
## 62 9.32 5.42 3.42 13.29 16.9 102 testavg
## 63 24.65 60 60 12.8 37.1 223 testavg
## 64 39.41 60 19.82 60 60.0 360 testavg
## 65 24.94 13.03 7.44 9.08 25.5 153 control
## 66 34.62 41.24 14.73 28.74 27.5 165 control
## 67 15.31 33.44 7.82 5.64 40.2 241 control
## 68 13.29 8.58 6.79 3.3 36.1 217 control
## 69 27.75 16.84 5.66 11.86 30.2 181 control
## 70 7 9.88 16.86 15.48 23.0 138 control
## 71 35.42 17.34 12.72 20.34 41.5 249 control
## 72 <NA> <NA> 23.87 11.4 11.4 69 control
## 73 7.58 35.76 7.67 13.08 31.4 188 control
## 74 45.55 8.28 13.88 13.26 47.1 282 control
## 75 11.97 9.93 6.78 6.73 38.6 232 control
## 76 37.1 24.34 7.87 44.71 31.0 186 control
## 77 60 29.45 35.41 9.88 30.5 183 control
## 78 9.06 20.53 8.58 14.72 25.4 152 control
## 79 14.22 8.76 15.34 9.49 11.0 66 control
## 80 7.16 7.32 15.01 7.26 9.8 58 control
## 81 37.22 24.93 16.09 6.8 40.4 242 control
## 82 19.04 21.22 12.79 7.87 28.7 172 control
## 83 60 26.16 35.51 12.48 41.1 246 control
## 84 25.47 8.33 6.91 6.98 43.6 262 control
## 85 56.8 15.89 4.07 4.19 34.2 205 control
## 86 16.18 20.07 <NA> <NA> 38.3 230 control
## 87 25.27 24.22 6.72 37.59 29.1 175 control
## 88 12.87 40.06 34.31 24.09 26.2 157 control
## 89 16.84 4.09 <NA> <NA> 37.1 223 control
## 90 17.99 21.39 <NA> <NA> 33.6 202 control
## 91 19.92 18.07 8.63 5.69 43.1 258 control
## 92 6.11 25.01 41.03 7.34 NA NA control
## 93 28.38 15.62 16.46 4.27 30.2 181 control
## 94 9.32 5.42 3.42 13.29 16.9 102 control
## 95 24.65 60 60 12.8 37.1 223 control
## 96 39.41 60 19.82 60 60.0 360 control
## 97 24.94 13.03 7.44 9.08 25.5 153 control
## 98 34.62 41.24 14.73 28.74 27.5 165 control
## 99 15.31 33.44 7.82 5.64 40.2 241 control
## 100 13.29 8.58 6.79 3.3 36.1 217 control
## 101 27.75 16.84 5.66 11.86 30.2 181 control
## 102 7 9.88 16.86 15.48 23.0 138 control
## 103 35.42 17.34 12.72 20.34 41.5 249 control
## 104 <NA> <NA> 23.87 11.4 11.4 69 control
## 105 7.58 35.76 7.67 13.08 31.4 188 control
## 106 45.55 8.28 13.88 13.26 47.1 282 control
## 107 11.97 9.93 6.78 6.73 38.6 232 control
## 108 37.1 24.34 7.87 44.71 31.0 186 control
## 109 60 29.45 35.41 9.88 30.5 183 control
## 110 9.06 20.53 8.58 14.72 25.4 152 control
## 111 14.22 8.76 15.34 9.49 11.0 66 control
## 112 7.16 7.32 15.01 7.26 9.8 58 control
## 113 37.22 24.93 16.09 6.8 40.4 242 control
## 114 19.04 21.22 12.79 7.87 28.7 172 control
## 115 60 26.16 35.51 12.48 41.1 246 control
## 116 25.47 8.33 6.91 6.98 43.6 262 control
## 117 56.8 15.89 4.07 4.19 34.2 205 control
## 118 16.18 20.07 <NA> <NA> 38.3 230 control
## 119 25.27 24.22 6.72 37.59 29.1 175 control
## 120 12.87 40.06 34.31 24.09 26.2 157 control
## 121 16.84 4.09 <NA> <NA> 37.1 223 control
## 122 17.99 21.39 <NA> <NA> 33.6 202 control
## 123 19.92 18.07 8.63 5.69 43.1 258 control
## 124 6.11 25.01 41.03 7.34 NA NA control
## 125 28.38 15.62 16.46 4.27 30.2 181 control
## 126 9.32 5.42 3.42 13.29 16.9 102 control
## 127 24.65 60 60 12.8 37.1 223 control
## 128 39.41 60 19.82 60 60.0 360 control
## 129 47.26 60 60 60 49.4 296 testavg
## 130 55.15 9.18 7.31 5 45.0 270 testavg
## 131 NA - 16.388 16.39 32.5 13.67 31.0 186 testavg
## 132 15.1 16.29 9.45 NA - 3.468 60.0 360 testavg
## 133 31.12 5.81 NA - 3.604 NA - 2.108 40.3 242 testavg
## 134 29.44 31.62 5.41 22.54 8.7 52 testavg
## 135 9.28 21.25 8.36 8.7 22.3 134 testavg
## 136 60 60 NA - 1.292 NA - 1.088 53.8 323 testavg
## 137 39.85 60 60 32.78 50.3 302 testavg
## 138 6.66 6.39 NA - 2.72 NA - 2.788 35.8 215 testavg
## 139 NA - 2.21 8.19 14.01 7.04 41.2 247 testavg
## 140 9.79 11.29 5.75 NA - 3.808 18.2 109 testavg
## 141 40.83 12.95 15.16 12.07 24.0 144 testavg
## 142 60 17.92 44.71 17.58 46.3 278 testavg
## 143 5.13 NA-4.046 19.18 10 28.2 169 testavg
## 144 6.56 4.69 16.32 20.3 39.2 235 testavg
## 145 11.49 40.43 9.66 NA - 2.822 14.5 87 testavg
## 146 NA-3.06 60 45.73 19.89 30.5 183 testavg
## 147 16.22 23.49 10.47 10.2 35.1 210 testavg
## 148 28.46 60 25.51 10.88 47.1 283 testavg
## 149 19.52 NA - 4.182 36.52 39.2 34.6 208 testavg
## 150 14.55 5.17 18.29 Fuss out 23.6 142 testavg
## 151 14.62 8.64 14.52 10 29.9 180 testavg
## 152 3.67 12.41 54.98 60 41.1 246 testavg
## 153 9.96 9.93 14.79 9.93 24.0 144 testavg
## 154 9.66 9.66 17.24 11.93 32.4 194 testavg
## 155 12.04 26.59 5.47 12.51 44.8 269 testavg
## 156 26.04 18.7 34.14 38.62 42.3 254 testavg
## 157 11.73 6.56 16.66 9.01 36.6 220 testavg
## 158 31.82 8.36 9.08 21.22 36.7 220 testavg
## 159 47.26 60 60 60 49.4 296 testavg
## 160 55.15 9.18 7.31 5 45.0 270 testavg
## 161 NA - 16.388 16.39 32.5 13.67 31.0 186 testavg
## 162 15.1 16.29 9.45 NA - 3.468 60.0 360 testavg
## 163 31.12 5.81 NA - 3.604 NA - 2.108 40.3 242 testavg
## 164 29.44 31.62 5.41 22.54 8.7 52 testavg
## 165 9.28 21.25 8.36 8.7 22.3 134 testavg
## 166 60 60 NA - 1.292 NA - 1.088 53.8 323 testavg
## 167 39.85 60 60 32.78 50.3 302 testavg
## 168 6.66 6.39 NA - 2.72 NA - 2.788 35.8 215 testavg
## 169 NA - 2.21 8.19 14.01 7.04 41.2 247 testavg
## 170 9.79 11.29 5.75 NA - 3.808 18.2 109 testavg
## 171 40.83 12.95 15.16 12.07 24.0 144 testavg
## 172 60 17.92 44.71 17.58 46.3 278 testavg
## 173 5.13 NA-4.046 19.18 10 28.2 169 testavg
## 174 6.56 4.69 16.32 20.3 39.2 235 testavg
## 175 11.49 40.43 9.66 NA - 2.822 14.5 87 testavg
## 176 NA-3.06 60 45.73 19.89 30.5 183 testavg
## 177 16.22 23.49 10.47 10.2 35.1 210 testavg
## 178 28.46 60 25.51 10.88 47.1 283 testavg
## 179 19.52 NA - 4.182 36.52 39.2 34.6 208 testavg
## 180 14.55 5.17 18.29 Fuss out 23.6 142 testavg
## 181 14.62 8.64 14.52 10 29.9 180 testavg
## 182 3.67 12.41 54.98 60 41.1 246 testavg
## 183 9.96 9.93 14.79 9.93 24.0 144 testavg
## 184 9.66 9.66 17.24 11.93 32.4 194 testavg
## 185 12.04 26.59 5.47 12.51 44.8 269 testavg
## 186 26.04 18.7 34.14 38.62 42.3 254 testavg
## 187 11.73 6.56 16.66 9.01 36.6 220 testavg
## 188 31.82 8.36 9.08 21.22 36.7 220 testavg
## 189 47.26 60 60 60 49.4 296 control
## 190 55.15 9.18 7.31 5 45.0 270 control
## 191 NA - 16.388 16.39 32.5 13.67 31.0 186 control
## 192 15.1 16.29 9.45 NA - 3.468 60.0 360 control
## 193 31.12 5.81 NA - 3.604 NA - 2.108 40.3 242 control
## 194 29.44 31.62 5.41 22.54 8.7 52 control
## 195 9.28 21.25 8.36 8.7 22.3 134 control
## 196 60 60 NA - 1.292 NA - 1.088 53.8 323 control
## 197 39.85 60 60 32.78 50.3 302 control
## 198 6.66 6.39 NA - 2.72 NA - 2.788 35.8 215 control
## 199 NA - 2.21 8.19 14.01 7.04 41.2 247 control
## 200 9.79 11.29 5.75 NA - 3.808 18.2 109 control
## 201 40.83 12.95 15.16 12.07 24.0 144 control
## 202 60 17.92 44.71 17.58 46.3 278 control
## 203 5.13 NA-4.046 19.18 10 28.2 169 control
## 204 6.56 4.69 16.32 20.3 39.2 235 control
## 205 11.49 40.43 9.66 NA - 2.822 14.5 87 control
## 206 NA-3.06 60 45.73 19.89 30.5 183 control
## 207 16.22 23.49 10.47 10.2 35.1 210 control
## 208 28.46 60 25.51 10.88 47.1 283 control
## 209 19.52 NA - 4.182 36.52 39.2 34.6 208 control
## 210 14.55 5.17 18.29 Fuss out 23.6 142 control
## 211 14.62 8.64 14.52 10 29.9 180 control
## 212 3.67 12.41 54.98 60 41.1 246 control
## 213 9.96 9.93 14.79 9.93 24.0 144 control
## 214 9.66 9.66 17.24 11.93 32.4 194 control
## 215 12.04 26.59 5.47 12.51 44.8 269 control
## 216 26.04 18.7 34.14 38.62 42.3 254 control
## 217 11.73 6.56 16.66 9.01 36.6 220 control
## 218 31.82 8.36 9.08 21.22 36.7 220 control
## 219 47.26 60 60 60 49.4 296 control
## 220 55.15 9.18 7.31 5 45.0 270 control
## 221 NA - 16.388 16.39 32.5 13.67 31.0 186 control
## 222 15.1 16.29 9.45 NA - 3.468 60.0 360 control
## 223 31.12 5.81 NA - 3.604 NA - 2.108 40.3 242 control
## 224 29.44 31.62 5.41 22.54 8.7 52 control
## 225 9.28 21.25 8.36 8.7 22.3 134 control
## 226 60 60 NA - 1.292 NA - 1.088 53.8 323 control
## 227 39.85 60 60 32.78 50.3 302 control
## 228 6.66 6.39 NA - 2.72 NA - 2.788 35.8 215 control
## 229 NA - 2.21 8.19 14.01 7.04 41.2 247 control
## 230 9.79 11.29 5.75 NA - 3.808 18.2 109 control
## 231 40.83 12.95 15.16 12.07 24.0 144 control
## 232 60 17.92 44.71 17.58 46.3 278 control
## 233 5.13 NA-4.046 19.18 10 28.2 169 control
## 234 6.56 4.69 16.32 20.3 39.2 235 control
## 235 11.49 40.43 9.66 NA - 2.822 14.5 87 control
## 236 NA-3.06 60 45.73 19.89 30.5 183 control
## 237 16.22 23.49 10.47 10.2 35.1 210 control
## 238 28.46 60 25.51 10.88 47.1 283 control
## 239 19.52 NA - 4.182 36.52 39.2 34.6 208 control
## 240 14.55 5.17 18.29 Fuss out 23.6 142 control
## 241 14.62 8.64 14.52 10 29.9 180 control
## 242 3.67 12.41 54.98 60 41.1 246 control
## 243 9.96 9.93 14.79 9.93 24.0 144 control
## 244 9.66 9.66 17.24 11.93 32.4 194 control
## 245 12.04 26.59 5.47 12.51 44.8 269 control
## 246 26.04 18.7 34.14 38.62 42.3 254 control
## 247 11.73 6.56 16.66 9.01 36.6 220 control
## 248 31.82 8.36 9.08 21.22 36.7 220 control
## 249 6.138 9.362 10.478 2.79 38.0 190 testavg
## 250 7.75 46.19 32.054 15.066 55.2 276 testavg
## 251 14.694 8.866 NA_9.362 NA_5.89 52.5 262 testavg
## 252 48.174 44.888 22.444 15.934 58.9 353 testavg
## 253 21.39 46.376 6.944 24.924 34.9 209 testavg
## 254 16.678 9.424 8.37 15.128 14.7 74 testavg
## 255 14.26 13.144 7.936 44.578 43.7 175 testavg
## 256 35.34 16.244 5.146 6.014 25.5 153 testavg
## 257 46.872 4.464 45.787 9.24 18.5 74 testavg
## 258 58.548 38.511 13.563 43.263 33.9 135 testavg
## 259 20.026 12.152 NA_3.038 NA_2.232 40.5 243 testavg
## 260 9.92 6.758 9.238 7.378 42.2 253 testavg
## 261 14.012 7.874 7.936 6.386 30.2 151 testavg
## 262 8.432 9.238 5.58 14.078 53.9 324 testavg
## 263 29.264 16.12 51.15 15.934 39.1 196 testavg
## 264 56.156 43.448 8.866 11.346 47.7 239 testavg
## 265 22.32 9.734 10.044 10.602 30.0 90 testavg
## 266 57.458 6.324 5.828 16.678 36.4 218 testavg
## 267 11.222 14.074 5.704 21.948 36.3 218 testavg
## 268 14.632 26.784 13.02 6.882 16.5 99 testavg
## 269 12.214 4.898 8.556 6.448 11.4 69 testavg
## 270 21.452 4.526 25.544 11.098 24.3 97 testavg
## 271 7.998 40.052 6.696 15.996 44.7 223 testavg
## 272 32.302 23.126 8.99 11.408 27.2 163 testavg
## 273 12.772 9.858 15.438 18.228 48.3 290 testavg
## 274 43.71 16.306 8.432 19.034 60.0 360 testavg
## 275 13.888 13.516 NA_7.502 NA_2.418 52.9 264 testavg
## 276 43.462 7.192 31.248 14.694 33.4 200 testavg
## 277 NA_19.406 NA_13.764 8.122 6.51 30.2 121 testavg
## 278 25.41 15.048 NA_31.152 NA_2.673 55.2 331 testavg
## 279 14.694 16.616 13.64 5.89 29.0 145 testavg
## 280 9.796 8.68 14.322 17.236 32.0 192 testavg
## 281 17.98 13.888 NA_9.486 NA_8.246 39.2 196 testavg
## 282 9.92 19.344 NA_20.894 NA_15.004 32.0 160 testavg
## 283 9.424 24.118 8.246 7.068 12.1 73 testavg
## 284 31.744 19.22 12.648 38.75 32.3 194 testavg
## 285 19.84 9.672 NA_18.476 NA_20.77 33.7 169 testavg
## 286 9.176 5.828 3.844 3.41 37.7 151 testavg
## 287 8.618 13.268 8.68 22.382 25.5 153 testavg
## 288 NA_21.018 NA_5.084 28.148 5.766 30.0 150 testavg
## 289 6.138 9.362 10.478 2.79 38.0 190 testavg
## 290 7.75 46.19 32.054 15.066 55.2 276 testavg
## 291 14.694 8.866 NA_9.362 NA_5.89 52.5 262 testavg
## 292 48.174 44.888 22.444 15.934 58.9 353 testavg
## 293 21.39 46.376 6.944 24.924 34.9 209 testavg
## 294 16.678 9.424 8.37 15.128 14.7 74 testavg
## 295 14.26 13.144 7.936 44.578 43.7 175 testavg
## 296 35.34 16.244 5.146 6.014 25.5 153 testavg
## 297 46.872 4.464 45.787 9.24 18.5 74 testavg
## 298 58.548 38.511 13.563 43.263 33.9 135 testavg
## 299 20.026 12.152 NA_3.038 NA_2.232 40.5 243 testavg
## 300 9.92 6.758 9.238 7.378 42.2 253 testavg
## 301 14.012 7.874 7.936 6.386 30.2 151 testavg
## 302 8.432 9.238 5.58 14.078 53.9 324 testavg
## 303 29.264 16.12 51.15 15.934 39.1 196 testavg
## 304 56.156 43.448 8.866 11.346 47.7 239 testavg
## 305 22.32 9.734 10.044 10.602 30.0 90 testavg
## 306 57.458 6.324 5.828 16.678 36.4 218 testavg
## 307 11.222 14.074 5.704 21.948 36.3 218 testavg
## 308 14.632 26.784 13.02 6.882 16.5 99 testavg
## 309 12.214 4.898 8.556 6.448 11.4 69 testavg
## 310 21.452 4.526 25.544 11.098 24.3 97 testavg
## 311 7.998 40.052 6.696 15.996 44.7 223 testavg
## 312 32.302 23.126 8.99 11.408 27.2 163 testavg
## 313 12.772 9.858 15.438 18.228 48.3 290 testavg
## 314 43.71 16.306 8.432 19.034 60.0 360 testavg
## 315 13.888 13.516 NA_7.502 NA_2.418 52.9 264 testavg
## 316 43.462 7.192 31.248 14.694 33.4 200 testavg
## 317 NA_19.406 NA_13.764 8.122 6.51 30.2 121 testavg
## 318 25.41 15.048 NA_31.152 NA_2.673 55.2 331 testavg
## 319 14.694 16.616 13.64 5.89 29.0 145 testavg
## 320 9.796 8.68 14.322 17.236 32.0 192 testavg
## 321 17.98 13.888 NA_9.486 NA_8.246 39.2 196 testavg
## 322 9.92 19.344 NA_20.894 NA_15.004 32.0 160 testavg
## 323 9.424 24.118 8.246 7.068 12.1 73 testavg
## 324 31.744 19.22 12.648 38.75 32.3 194 testavg
## 325 19.84 9.672 NA_18.476 NA_20.77 33.7 169 testavg
## 326 9.176 5.828 3.844 3.41 37.7 151 testavg
## 327 8.618 13.268 8.68 22.382 25.5 153 testavg
## 328 NA_21.018 NA_5.084 28.148 5.766 30.0 150 testavg
## 329 6.138 9.362 10.478 2.79 38.0 190 control
## 330 7.75 46.19 32.054 15.066 55.2 276 control
## 331 14.694 8.866 NA_9.362 NA_5.89 52.5 262 control
## 332 48.174 44.888 22.444 15.934 58.9 353 control
## 333 21.39 46.376 6.944 24.924 34.9 209 control
## 334 16.678 9.424 8.37 15.128 14.7 74 control
## 335 14.26 13.144 7.936 44.578 43.7 175 control
## 336 35.34 16.244 5.146 6.014 25.5 153 control
## 337 46.872 4.464 45.787 9.24 18.5 74 control
## 338 58.548 38.511 13.563 43.263 33.9 135 control
## 339 20.026 12.152 NA_3.038 NA_2.232 40.5 243 control
## 340 9.92 6.758 9.238 7.378 42.2 253 control
## 341 14.012 7.874 7.936 6.386 30.2 151 control
## 342 8.432 9.238 5.58 14.078 53.9 324 control
## 343 29.264 16.12 51.15 15.934 39.1 196 control
## 344 56.156 43.448 8.866 11.346 47.7 239 control
## 345 22.32 9.734 10.044 10.602 30.0 90 control
## 346 57.458 6.324 5.828 16.678 36.4 218 control
## 347 11.222 14.074 5.704 21.948 36.3 218 control
## 348 14.632 26.784 13.02 6.882 16.5 99 control
## 349 12.214 4.898 8.556 6.448 11.4 69 control
## 350 21.452 4.526 25.544 11.098 24.3 97 control
## 351 7.998 40.052 6.696 15.996 44.7 223 control
## 352 32.302 23.126 8.99 11.408 27.2 163 control
## 353 12.772 9.858 15.438 18.228 48.3 290 control
## 354 43.71 16.306 8.432 19.034 60.0 360 control
## 355 13.888 13.516 NA_7.502 NA_2.418 52.9 264 control
## 356 43.462 7.192 31.248 14.694 33.4 200 control
## 357 NA_19.406 NA_13.764 8.122 6.51 30.2 121 control
## 358 25.41 15.048 NA_31.152 NA_2.673 55.2 331 control
## 359 14.694 16.616 13.64 5.89 29.0 145 control
## 360 9.796 8.68 14.322 17.236 32.0 192 control
## 361 17.98 13.888 NA_9.486 NA_8.246 39.2 196 control
## 362 9.92 19.344 NA_20.894 NA_15.004 32.0 160 control
## 363 9.424 24.118 8.246 7.068 12.1 73 control
## 364 31.744 19.22 12.648 38.75 32.3 194 control
## 365 19.84 9.672 NA_18.476 NA_20.77 33.7 169 control
## 366 9.176 5.828 3.844 3.41 37.7 151 control
## 367 8.618 13.268 8.68 22.382 25.5 153 control
## 368 NA_21.018 NA_5.084 28.148 5.766 30.0 150 control
## 369 6.138 9.362 10.478 2.79 38.0 190 control
## 370 7.75 46.19 32.054 15.066 55.2 276 control
## 371 14.694 8.866 NA_9.362 NA_5.89 52.5 262 control
## 372 48.174 44.888 22.444 15.934 58.9 353 control
## 373 21.39 46.376 6.944 24.924 34.9 209 control
## 374 16.678 9.424 8.37 15.128 14.7 74 control
## 375 14.26 13.144 7.936 44.578 43.7 175 control
## 376 35.34 16.244 5.146 6.014 25.5 153 control
## 377 46.872 4.464 45.787 9.24 18.5 74 control
## 378 58.548 38.511 13.563 43.263 33.9 135 control
## 379 20.026 12.152 NA_3.038 NA_2.232 40.5 243 control
## 380 9.92 6.758 9.238 7.378 42.2 253 control
## 381 14.012 7.874 7.936 6.386 30.2 151 control
## 382 8.432 9.238 5.58 14.078 53.9 324 control
## 383 29.264 16.12 51.15 15.934 39.1 196 control
## 384 56.156 43.448 8.866 11.346 47.7 239 control
## 385 22.32 9.734 10.044 10.602 30.0 90 control
## 386 57.458 6.324 5.828 16.678 36.4 218 control
## 387 11.222 14.074 5.704 21.948 36.3 218 control
## 388 14.632 26.784 13.02 6.882 16.5 99 control
## 389 12.214 4.898 8.556 6.448 11.4 69 control
## 390 21.452 4.526 25.544 11.098 24.3 97 control
## 391 7.998 40.052 6.696 15.996 44.7 223 control
## 392 32.302 23.126 8.99 11.408 27.2 163 control
## 393 12.772 9.858 15.438 18.228 48.3 290 control
## 394 43.71 16.306 8.432 19.034 60.0 360 control
## 395 13.888 13.516 NA_7.502 NA_2.418 52.9 264 control
## 396 43.462 7.192 31.248 14.694 33.4 200 control
## 397 NA_19.406 NA_13.764 8.122 6.51 30.2 121 control
## 398 25.41 15.048 NA_31.152 NA_2.673 55.2 331 control
## 399 14.694 16.616 13.64 5.89 29.0 145 control
## 400 9.796 8.68 14.322 17.236 32.0 192 control
## 401 17.98 13.888 NA_9.486 NA_8.246 39.2 196 control
## 402 9.92 19.344 NA_20.894 NA_15.004 32.0 160 control
## 403 9.424 24.118 8.246 7.068 12.1 73 control
## 404 31.744 19.22 12.648 38.75 32.3 194 control
## 405 19.84 9.672 NA_18.476 NA_20.77 33.7 169 control
## 406 9.176 5.828 3.844 3.41 37.7 151 control
## 407 8.618 13.268 8.68 22.382 25.5 153 control
## 408 NA_21.018 NA_5.084 28.148 5.766 30.0 150 control
## type look loglook agegroup
## 1 lower 16.2 2.8 younger
## 2 lower 24.7 3.2 younger
## 3 lower 19.5 3.0 younger
## 4 lower 10.0 2.3 younger
## 5 lower 14.3 2.7 younger
## 6 lower 11.9 2.5 younger
## 7 lower 24.1 3.2 younger
## 8 lower 23.9 3.2 younger
## 9 lower 24.4 3.2 younger
## 10 lower 29.7 3.4 younger
## 11 lower 9.4 2.2 younger
## 12 lower 22.5 3.1 younger
## 13 lower 19.7 3.0 younger
## 14 lower 17.6 2.9 younger
## 15 lower 14.8 2.7 younger
## 16 lower 11.1 2.4 younger
## 17 lower 15.9 2.8 younger
## 18 lower 14.6 2.7 younger
## 19 lower 19.3 3.0 younger
## 20 lower 7.7 2.0 younger
## 21 lower 30.4 3.4 younger
## 22 lower 20.1 3.0 younger
## 23 lower 16.0 2.8 younger
## 24 lower 32.1 3.5 younger
## 25 lower 4.1 1.4 younger
## 26 lower 21.4 3.1 younger
## 27 lower 14.3 2.7 younger
## 28 lower 16.2 2.8 younger
## 29 lower 9.9 2.3 younger
## 30 lower 6.4 1.9 younger
## 31 lower 42.3 3.7 younger
## 32 lower 60.0 4.1 younger
## 33 higher 11.1 2.4 younger
## 34 higher 35.0 3.6 younger
## 35 higher 11.6 2.4 younger
## 36 higher 5.9 1.8 younger
## 37 higher 16.7 2.8 younger
## 38 higher 12.7 2.5 younger
## 39 higher 18.8 2.9 younger
## 40 higher 11.4 2.4 younger
## 41 higher 7.6 2.0 younger
## 42 higher 10.8 2.4 younger
## 43 higher 8.3 2.1 younger
## 44 higher 34.5 3.5 younger
## 45 higher 47.7 3.9 younger
## 46 higher 8.8 2.2 younger
## 47 higher 9.1 2.2 younger
## 48 higher 7.3 2.0 younger
## 49 higher 26.7 3.3 younger
## 50 higher 15.9 2.8 younger
## 51 higher 47.8 3.9 younger
## 52 higher 16.2 2.8 younger
## 53 higher 10.0 2.3 younger
## 54 higher 16.2 2.8 younger
## 55 higher 30.9 3.4 younger
## 56 higher 23.6 3.2 younger
## 57 higher 16.8 2.8 younger
## 58 higher 18.0 2.9 younger
## 59 higher 11.9 2.5 younger
## 60 higher 23.6 3.2 younger
## 61 higher 22.4 3.1 younger
## 62 higher 9.4 2.2 younger
## 63 higher 36.4 3.6 younger
## 64 higher 29.6 3.4 younger
## 65 shallow NA NA younger
## 66 shallow NA NA younger
## 67 shallow NA NA younger
## 68 shallow NA NA younger
## 69 shallow NA NA younger
## 70 shallow NA NA younger
## 71 shallow NA NA younger
## 72 shallow NA NA younger
## 73 shallow NA NA younger
## 74 shallow NA NA younger
## 75 shallow NA NA younger
## 76 shallow NA NA younger
## 77 shallow NA NA younger
## 78 shallow NA NA younger
## 79 shallow NA NA younger
## 80 shallow NA NA younger
## 81 shallow NA NA younger
## 82 shallow NA NA younger
## 83 shallow NA NA younger
## 84 shallow NA NA younger
## 85 shallow NA NA younger
## 86 shallow NA NA younger
## 87 shallow NA NA younger
## 88 shallow NA NA younger
## 89 shallow NA NA younger
## 90 shallow NA NA younger
## 91 shallow NA NA younger
## 92 shallow NA NA younger
## 93 shallow NA NA younger
## 94 shallow NA NA younger
## 95 shallow NA NA younger
## 96 shallow NA NA younger
## 97 deep NA NA younger
## 98 deep NA NA younger
## 99 deep NA NA younger
## 100 deep NA NA younger
## 101 deep NA NA younger
## 102 deep NA NA younger
## 103 deep NA NA younger
## 104 deep NA NA younger
## 105 deep NA NA younger
## 106 deep NA NA younger
## 107 deep NA NA younger
## 108 deep NA NA younger
## 109 deep NA NA younger
## 110 deep NA NA younger
## 111 deep NA NA younger
## 112 deep NA NA younger
## 113 deep NA NA younger
## 114 deep NA NA younger
## 115 deep NA NA younger
## 116 deep NA NA younger
## 117 deep NA NA younger
## 118 deep NA NA younger
## 119 deep NA NA younger
## 120 deep NA NA younger
## 121 deep NA NA younger
## 122 deep NA NA younger
## 123 deep NA NA younger
## 124 deep NA NA younger
## 125 deep NA NA younger
## 126 deep NA NA younger
## 127 deep NA NA younger
## 128 deep NA NA younger
## 129 lower 53.6 4.0 younger
## 130 lower 31.2 3.4 younger
## 131 lower 15.0 2.7 younger
## 132 lower 12.3 2.5 younger
## 133 lower 5.8 1.8 younger
## 134 lower 17.4 2.9 younger
## 135 lower 8.8 2.2 younger
## 136 lower 60.0 4.1 younger
## 137 lower 46.4 3.8 younger
## 138 lower 6.7 1.9 younger
## 139 lower 7.6 2.0 younger
## 140 lower 7.8 2.1 younger
## 141 lower 12.5 2.5 younger
## 142 lower 17.8 2.9 younger
## 143 lower 12.2 2.5 younger
## 144 lower 12.5 2.5 younger
## 145 lower 10.6 2.4 younger
## 146 lower 40.0 3.7 younger
## 147 lower 16.9 2.8 younger
## 148 lower 35.4 3.6 younger
## 149 lower 28.0 3.3 younger
## 150 lower 5.2 1.6 younger
## 151 lower 14.6 2.7 younger
## 152 lower 29.3 3.4 younger
## 153 lower 12.4 2.5 younger
## 154 lower 10.8 2.4 younger
## 155 lower 19.6 3.0 younger
## 156 lower 30.1 3.4 younger
## 157 lower 14.2 2.7 younger
## 158 lower 14.8 2.7 younger
## 159 higher 60.0 4.1 younger
## 160 higher 7.1 2.0 younger
## 161 higher 32.5 3.5 younger
## 162 higher 16.3 2.8 younger
## 163 higher 31.1 3.4 younger
## 164 higher 27.1 3.3 younger
## 165 higher 15.0 2.7 younger
## 166 higher 60.0 4.1 younger
## 167 higher 49.9 3.9 younger
## 168 higher 6.4 1.9 younger
## 169 higher 14.0 2.6 younger
## 170 higher 11.3 2.4 younger
## 171 higher 28.0 3.3 younger
## 172 higher 52.4 4.0 younger
## 173 higher 10.0 2.3 younger
## 174 higher 11.4 2.4 younger
## 175 higher 40.4 3.7 younger
## 176 higher 45.7 3.8 younger
## 177 higher 13.3 2.6 younger
## 178 higher 27.0 3.3 younger
## 179 higher 39.2 3.7 younger
## 180 higher 16.4 2.8 younger
## 181 higher 9.3 2.2 younger
## 182 higher 36.2 3.6 younger
## 183 higher 9.9 2.3 younger
## 184 higher 13.4 2.6 younger
## 185 higher 8.8 2.2 younger
## 186 higher 28.7 3.4 younger
## 187 higher 7.8 2.1 younger
## 188 higher 20.4 3.0 younger
## 189 shallow 20.8 3.0 younger
## 190 shallow 6.4 1.9 younger
## 191 shallow 14.6 2.7 younger
## 192 shallow 5.7 1.7 younger
## 193 shallow 15.0 2.7 younger
## 194 shallow 18.2 2.9 younger
## 195 shallow 8.4 2.1 younger
## 196 shallow 53.7 4.0 younger
## 197 shallow 22.0 3.1 younger
## 198 shallow 23.4 3.2 younger
## 199 shallow 9.6 2.3 younger
## 200 shallow 14.6 2.7 younger
## 201 shallow 19.3 3.0 younger
## 202 shallow 13.0 2.6 younger
## 203 shallow 5.8 1.8 younger
## 204 shallow 11.3 2.4 younger
## 205 shallow 4.4 1.5 younger
## 206 shallow 9.3 2.2 younger
## 207 shallow 19.9 3.0 younger
## 208 shallow 26.0 3.3 younger
## 209 shallow 6.8 1.9 younger
## 210 shallow 11.9 2.5 younger
## 211 shallow 10.3 2.3 younger
## 212 shallow 7.3 2.0 younger
## 213 shallow 4.8 1.6 younger
## 214 shallow 10.9 2.4 younger
## 215 shallow 13.5 2.6 younger
## 216 shallow 27.3 3.3 younger
## 217 shallow 21.6 3.1 younger
## 218 shallow 4.6 1.5 younger
## 219 deep 26.1 3.3 younger
## 220 deep 4.6 1.5 younger
## 221 deep 20.8 3.0 younger
## 222 deep 5.4 1.7 younger
## 223 deep 11.0 2.4 younger
## 224 deep 10.2 2.3 younger
## 225 deep 9.8 2.3 younger
## 226 deep 17.0 2.8 younger
## 227 deep 3.2 1.2 younger
## 228 deep 10.0 2.3 younger
## 229 deep 39.3 3.7 younger
## 230 deep 13.2 2.6 younger
## 231 deep 8.2 2.1 younger
## 232 deep 12.6 2.5 younger
## 233 deep 10.5 2.3 younger
## 234 deep 5.8 1.8 younger
## 235 deep 5.8 1.8 younger
## 236 deep 7.6 2.0 younger
## 237 deep 12.0 2.5 younger
## 238 deep 14.2 2.7 younger
## 239 deep 17.6 2.9 younger
## 240 deep 6.9 1.9 younger
## 241 deep 16.3 2.8 younger
## 242 deep 5.9 1.8 younger
## 243 deep 19.2 3.0 younger
## 244 deep 19.1 3.0 younger
## 245 deep 6.0 1.8 younger
## 246 deep 16.1 2.8 younger
## 247 deep 22.9 3.1 younger
## 248 deep 4.7 1.5 younger
## 249 lower 6.1 1.8 younger
## 250 lower 30.6 3.4 younger
## 251 lower 8.9 2.2 younger
## 252 lower 30.4 3.4 younger
## 253 lower 35.6 3.6 younger
## 254 lower 12.3 2.5 younger
## 255 lower 28.9 3.4 younger
## 256 lower 11.1 2.4 younger
## 257 lower 6.9 1.9 younger
## 258 lower 40.9 3.7 younger
## 259 lower 20.0 3.0 younger
## 260 lower 9.6 2.3 younger
## 261 lower 11.0 2.4 younger
## 262 lower 7.0 1.9 younger
## 263 lower 40.2 3.7 younger
## 264 lower 32.5 3.5 younger
## 265 lower 16.2 2.8 younger
## 266 lower 31.6 3.5 younger
## 267 lower 8.5 2.1 younger
## 268 lower 13.8 2.6 younger
## 269 lower 10.4 2.3 younger
## 270 lower 23.5 3.2 younger
## 271 lower 7.3 2.0 younger
## 272 lower 20.6 3.0 younger
## 273 lower 14.1 2.6 younger
## 274 lower 26.1 3.3 younger
## 275 lower 13.9 2.6 younger
## 276 lower 37.4 3.6 younger
## 277 lower 8.1 2.1 younger
## 278 lower 25.4 3.2 younger
## 279 lower 11.3 2.4 younger
## 280 lower 13.0 2.6 younger
## 281 lower 13.9 2.6 younger
## 282 lower 19.3 3.0 younger
## 283 lower 15.6 2.7 younger
## 284 lower 29.0 3.4 younger
## 285 lower 9.7 2.3 younger
## 286 lower 4.6 1.5 younger
## 287 lower 17.8 2.9 younger
## 288 lower 5.8 1.8 younger
## 289 higher 8.3 2.1 younger
## 290 higher 19.9 3.0 younger
## 291 higher 14.7 2.7 younger
## 292 higher 35.3 3.6 younger
## 293 higher 14.2 2.7 younger
## 294 higher 12.5 2.5 younger
## 295 higher 11.1 2.4 younger
## 296 higher 20.2 3.0 younger
## 297 higher 46.3 3.8 younger
## 298 higher 36.1 3.6 younger
## 299 higher 12.2 2.5 younger
## 300 higher 7.1 2.0 younger
## 301 higher 7.1 2.0 younger
## 302 higher 11.7 2.5 younger
## 303 higher 16.0 2.8 younger
## 304 higher 27.4 3.3 younger
## 305 higher 10.2 2.3 younger
## 306 higher 11.5 2.4 younger
## 307 higher 18.0 2.9 younger
## 308 higher 16.8 2.8 younger
## 309 higher 5.7 1.7 younger
## 310 higher 7.8 2.1 younger
## 311 higher 28.0 3.3 younger
## 312 higher 17.3 2.8 younger
## 313 higher 14.0 2.6 younger
## 314 higher 17.7 2.9 younger
## 315 higher 13.5 2.6 younger
## 316 higher 10.9 2.4 younger
## 317 higher 6.5 1.9 younger
## 318 higher 15.0 2.7 younger
## 319 higher 14.2 2.7 younger
## 320 higher 12.1 2.5 younger
## 321 higher 18.0 2.9 younger
## 322 higher 9.9 2.3 younger
## 323 higher 8.8 2.2 younger
## 324 higher 22.2 3.1 younger
## 325 higher 19.8 3.0 younger
## 326 higher 6.5 1.9 younger
## 327 higher 8.6 2.2 younger
## 328 higher 28.1 3.3 younger
## 329 shallow 6.2 1.8 younger
## 330 shallow 8.6 2.2 younger
## 331 shallow 6.0 1.8 younger
## 332 shallow 17.4 2.9 younger
## 333 shallow 10.7 2.4 younger
## 334 shallow 9.2 2.2 younger
## 335 shallow 8.6 2.2 younger
## 336 shallow 8.3 2.1 younger
## 337 shallow 4.0 1.4 younger
## 338 shallow 4.8 1.6 younger
## 339 shallow 21.8 3.1 younger
## 340 shallow 26.4 3.3 younger
## 341 shallow 12.1 2.5 younger
## 342 shallow 7.1 2.0 younger
## 343 shallow 16.1 2.8 younger
## 344 shallow 12.8 2.5 younger
## 345 shallow NA NA younger
## 346 shallow 23.0 3.1 younger
## 347 shallow 10.3 2.3 younger
## 348 shallow 9.8 2.3 younger
## 349 shallow 6.3 1.8 younger
## 350 shallow 6.6 1.9 younger
## 351 shallow 11.3 2.4 younger
## 352 shallow 23.1 3.1 younger
## 353 shallow 11.6 2.4 younger
## 354 shallow 21.9 3.1 younger
## 355 shallow 18.0 2.9 younger
## 356 shallow 9.9 2.3 younger
## 357 shallow 12.4 2.5 younger
## 358 shallow 19.1 3.0 younger
## 359 shallow 8.4 2.1 younger
## 360 shallow 22.4 3.1 younger
## 361 shallow 6.4 1.9 younger
## 362 shallow 15.3 2.7 younger
## 363 shallow 6.0 1.8 younger
## 364 shallow NA NA younger
## 365 shallow NA NA younger
## 366 shallow 5.1 1.6 younger
## 367 shallow 21.4 3.1 younger
## 368 shallow 7.0 1.9 younger
## 369 deep 21.1 3.0 younger
## 370 deep 9.5 2.3 younger
## 371 deep 5.0 1.6 younger
## 372 deep 13.0 2.6 younger
## 373 deep 20.1 3.0 younger
## 374 deep 11.7 2.5 younger
## 375 deep 31.4 3.4 younger
## 376 deep 8.4 2.1 younger
## 377 deep 14.1 2.6 younger
## 378 deep 4.0 1.4 younger
## 379 deep 6.3 1.8 younger
## 380 deep 9.7 2.3 younger
## 381 deep 6.9 1.9 younger
## 382 deep 9.4 2.2 younger
## 383 deep 7.6 2.0 younger
## 384 deep 37.7 3.6 younger
## 385 deep NA NA younger
## 386 deep 21.5 3.1 younger
## 387 deep 5.0 1.6 younger
## 388 deep 5.6 1.7 younger
## 389 deep 6.6 1.9 younger
## 390 deep 5.3 1.7 younger
## 391 deep 10.9 2.4 younger
## 392 deep 10.6 2.4 younger
## 393 deep 4.8 1.6 younger
## 394 deep 9.4 2.2 younger
## 395 deep 7.7 2.0 younger
## 396 deep 10.2 2.3 younger
## 397 deep 3.6 1.3 younger
## 398 deep 39.9 3.7 younger
## 399 deep 19.1 3.0 younger
## 400 deep 24.4 3.2 younger
## 401 deep 14.4 2.7 younger
## 402 deep 9.7 2.3 younger
## 403 deep 17.1 2.8 younger
## 404 deep 6.9 1.9 younger
## 405 deep NA NA younger
## 406 deep NA NA younger
## 407 deep 11.8 2.5 younger
## 408 deep 10.9 2.4 youngerrisk.avg <- long.avg %>%
filter(cost == "Risk") %>%
mutate(task = as.factor(
case_when(experiment == "RISK13" ~ "infer.value",
(experiment == "MR13" | experiment == "MR2") ~ "min.risk")))
op <- options(contrasts = c("contr.treatment", "contr.poly")) # treatment contrasts# function for identifying influential observations, and then returning a new model without them
# INPUTS: model = model name, data = dataset, and subj = column heading for observations
# OUTPUT: model excluding influential subjects
exclude.cooks <- function(model, data, subj) {
cooks <- cooks.distance(influence(model, subj))
cutoff <- 4/length(unique(data$subj))
new.model <- exclude.influence(model, grouping = subj, level=data[which(cooks > cutoff),]$subj)
return(new.model)
}
# function that computes CIs and returns them in df
gen.ci <- function(model) {
df <- data.frame(confint(model))
names(df) <- c("lower", "upper")
return(df)
}
# function that converts model summary to df
gen.m <- function(model) {
df <- data.frame(coef(summary(model)))
names(df) <- c("est", "se", "df", "t", "p")
return(df)
}
# function that returns column of standardized betas from lmer model
gen.beta <- function(model) {
f <- data.frame(fixef(model))
colnames(f) <- "beta"
return(f)
}
# function that returns age info and number of female infants in a dataset
info <- function(longdata) {
longdata %>%
group_by(subj) %>%
filter(row_number()==1) %>%
ungroup() %>%
summarize(mean = mean(agem), min=range(agem)[1], max=range(agem)[2], f=sum(sex=="f"), n=length(unique(subj)))
}
## Retrieved from : http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/#error-bars-for-within-subjects-variables
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
## data: a data frame.
## measurevar: the name of a column that contains the variable to be summariezed
## groupvars: a vector containing names of columns that contain grouping variables
## na.rm: a boolean that indicates whether to ignore NA's
## conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=TRUE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- plyr::rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
## Norms the data within specified groups in a data frame; it normalizes each
## subject (identified by idvar) so that they have the same mean, within each group
## specified by betweenvars.
## data: a data frame.
## idvar: the name of a column that identifies each subject (or matched subjects)
## measurevar: the name of a column that contains the variable to be summariezed
## betweenvars: a vector containing names of columns that are between-subjects variables
## na.rm: a boolean that indicates whether to ignore NA's
normDataWithin <- function(data=NULL, idvar, measurevar, betweenvars=NULL,
na.rm=TRUE, .drop=TRUE) {
library(plyr)
# Measure var on left, idvar + between vars on right of formula.
data.subjMean <- ddply(data, c(idvar, betweenvars), .drop=.drop,
.fun = function(xx, col, na.rm) {
c(subjMean = mean(xx[,col], na.rm=na.rm))
},
measurevar,
na.rm
)
# Put the subject means with original data
data <- merge(data, data.subjMean)
# Get the normalized data in a new column
measureNormedVar <- paste(measurevar, "_norm", sep="")
data[,measureNormedVar] <- data[,measurevar] - data[,"subjMean"] +
mean(data[,measurevar], na.rm=na.rm)
# Remove this subject mean column
data$subjMean <- NULL
return(data)
}
## Summarizes data, handling within-subjects variables by removing inter-subject variability.
## It will still work if there are no within-S variables.
## Gives count, un-normed mean, normed mean (with same between-group mean),
## standard deviation, standard error of the mean, and confidence interval.
## If there are within-subject variables, calculate adjusted values using method from Morey (2008).
## data: a data frame.
## measurevar: the name of a column that contains the variable to be summariezed
## betweenvars: a vector containing names of columns that are between-subjects variables
## withinvars: a vector containing names of columns that are within-subjects variables
## idvar: the name of a column that identifies each subject (or matched subjects)
## na.rm: a boolean that indicates whether to ignore NA's
## conf.interval: the percent range of the confidence interval (default is 95%)
summarySEwithin <- function(data=NULL, measurevar, betweenvars=NULL, withinvars=NULL,
idvar=NULL, na.rm=TRUE, conf.interval=.95, .drop=TRUE) {
# Ensure that the betweenvars and withinvars are factors
factorvars <- vapply(data[, c(betweenvars, withinvars), drop=FALSE],
FUN=is.factor, FUN.VALUE=logical(1))
if (!all(factorvars)) {
nonfactorvars <- names(factorvars)[!factorvars]
message("Automatically converting the following non-factors to factors: ",
paste(nonfactorvars, collapse = ", "))
data[nonfactorvars] <- lapply(data[nonfactorvars], factor)
}
# Get the means from the un-normed data
datac <- summarySE(data, measurevar, groupvars=c(betweenvars, withinvars),
na.rm=na.rm, conf.interval=conf.interval, .drop=.drop)
# Drop all the unused columns (these will be calculated with normed data)
datac$sd <- NULL
datac$se <- NULL
datac$ci <- NULL
# Norm each subject's data
ndata <- normDataWithin(data, idvar, measurevar, betweenvars, na.rm, .drop=.drop)
# This is the name of the new column
measurevar_n <- paste(measurevar, "_norm", sep="")
# Collapse the normed data - now we can treat between and within vars the same
ndatac <- summarySE(ndata, measurevar_n, groupvars=c(betweenvars, withinvars),
na.rm=na.rm, conf.interval=conf.interval, .drop=.drop)
# Apply correction from Morey (2008) to the standard error and confidence interval
# Get the product of the number of conditions of within-S variables
nWithinGroups <- prod(vapply(ndatac[,withinvars, drop=FALSE], FUN=nlevels,
FUN.VALUE=numeric(1)))
correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) )
# Apply the correction factor
ndatac$sd <- ndatac$sd * correctionFactor
ndatac$se <- ndatac$se * correctionFactor
ndatac$ci <- ndatac$ci * correctionFactor
# Combine the un-normed means with the normed results
merge(datac, ndatac)
}
# function that returns ICC
reporticc <- function(output, places) {
mainstat <- output$value
upperci <- output$ubound
lowerci <- output$lbound
statistic <- paste("ICC=", round(mainstat,places), ", 95% CI [", round(lowerci, places), ", ", round(upperci, places), "]", sep = "")
return(statistic)
}
# function that returns APA-formatted result from lme4/lmerTest table
# version 1 that reports ci, b, beta, se, p
report <- function(table, index, places, tails, flip) {
if (tails == "1") {
p.value <- round(table$p[index], 3)/2 # p values always rounded to 3 places
howmanytails <- "one-tailed"
} else {
p.value <- round(table$p[index], 3) # p values always rounded to 3 places
howmanytails <- "two-tailed"
}
if (p.value < .001) {
p <- "<.001"
} else {
p <- paste("=", str_remove(p.value, "^0+"), sep = "")
}
if (missing(flip)) {
result <- paste("[", round(table$lower[index], places), ",", round(table$upper[index], places), "], ß=", round(table$beta[index], places), ", B=", round(table$est[index],places), ", SE=", round(table$se[index],places), ", p", p, ", ", howmanytails, sep = "")
} else {
result <- paste("[", -round(table$upper[index], places), ",", -round(table$lower[index], places), "], ß=", -round(table$beta[index], places), ", B=", -round(table$est[index],places), ", SE=", round(table$se[index],places), ", p", p, ", ", howmanytails, sep = "")
}
return(result)
}
# version 2, more condensed, that reports ci, beta, t(df), p
report2 <- function(table, index, places, tails, flip) {
if (tails == "1") {
p.value <- round(table$p[index], 3)/2 # p values always rounded to 3 places
howmanytails <- "one-tailed"
} else {
p.value <- round(table$p[index], 3) # p values always rounded to 3 places
howmanytails <- "two-tailed"
}
if (p.value < .001) {
p <- "<.001"
} else {
p <- paste("=", str_remove(p.value, "^0+"), sep = "")
}
if (missing(flip)) {
result <- paste("[", round(table$lower[index], places), ",", round(table$upper[index], places), "], ß=", round(table$beta[index], places), ", t(", round(table$df[index],2), ")=", round(table$t[index], places), ", p", p, ", ", howmanytails, sep = "")
} else {
result <- paste("[", -round(table$upper[index], places), ",", -round(table$lower[index], places), "], ß=", -round(table$beta[index], places), ", t(", round(table$df[index],2), ")=", -round(table$t[index], places), ", p", p, ", ", howmanytails, sep = "")
}
return(result)
}## get within-subjects CIs for plotting
# warning about Nan has to do with missing observations for control events
summary.avg <- summarySEwithin(data = risk.avg, measurevar = "look", betweenvars = c("exp"), withinvars = c("type", "phase"), idvar = "subj") %>%
drop_na() %>%
mutate(cliff = type)
levels(summary.avg$cliff) <- c("deep", "deep", "shallow", "shallow")
levels(summary.avg$phase) <- c("control", "test")# figure out how many looks are missing from the dataframe
nexclude <- wide %>%
gather(type, look, control_1:test4) %>%
filter(cost=="Risk") %>%
mutate(missing = case_when(is.na(look) | str_detect(look, "NA") ~ 1)) %>%
group_by(exp) %>%
count(missing) %>%
filter(!is.na(missing)) %>%
rename(n_missing = n) %>%
select(!missing)
experiments <- c("Exp.1", "Exp.2", "Exp.3")
totaltrials.Exp1 <- wide %>%
gather(type, look, fam1:test4) %>% # no control trials
filter(cost=="Risk") %>%
filter(exp == "Exp.1") %>%
group_by(exp) %>%
tally() %>%
rename(total = n)
totaltrials.Exp23 <- wide %>%
gather(type, look, control_1:test4) %>%
filter(cost=="Risk") %>%
filter(exp == "Exp.2" | exp == "Exp.3") %>%
group_by(exp) %>%
tally() %>%
rename(total = n)
ntrials <- full_join(nexclude, rbind(totaltrials.Exp1, totaltrials.Exp23)) %>% na.omit()Reliability + Distribution Info
rel <- read.csv(file = "peril_reliability_deid.csv", header=TRUE)
exp1 <- rel %>% filter(experiment.paper=="Exp1")
exp2 <- rel %>% filter(experiment.paper=="Exp2")
exp3 <- rel %>% filter(experiment.paper=="Exp3")
exp4.study1 <- rel %>% filter(experiment.paper =="Exp4.Study1")
exp4.study2 <- rel %>% filter(experiment.paper =="Exp4.Study2")
exp4.study3 <- rel %>% filter(experiment.paper =="Exp4.Study3")
exp1rel <- icc(data.frame(exp1$secondary.look, exp1$orig.look),model="one", type="agreement")
exp2rel <- icc(data.frame(exp2$secondary.look, exp2$orig.look),model="one", type="agreement")
exp3rel <- icc(data.frame(exp3$secondary.look, exp3$orig.look),model="one", type="agreement")
exp4.study1.rel <- icc(data.frame(exp4.study1$secondary.look, exp4.study1$orig.look),model="one", type="agreement")
exp4.study2.rel <- icc(data.frame(exp4.study2$secondary.look, exp4.study2$orig.look),model="one", type="agreement")
exp4.study3.rel <- icc(data.frame(exp4.study3$secondary.look, exp4.study3$orig.look),model="one", type="agreement")normal.ll <- fitdistr(na.omit(risk.avg$look), "normal")$loglik
lognormal.ll <- fitdistr(na.omit(risk.avg$look), "lognormal")$loglikFigures
theme_set(theme_cowplot(font_size=20))
exp1.fig.data <- risk.avg %>% filter(task == "infer.value",
phase == "testavg")
levels(exp1.fig.data$type) <- c(NA, "higher", "lower", NA)
exp1.fig.data$type <- relevel(exp1.fig.data$type, ref = "higher")
colors1 <- c(wes_palettes$Zissou1[3], wes_palettes$Zissou1[2])
risk1 <- ggplot(data = exp1.fig.data %>% filter(exp=="Exp.1"), aes(type, look, fill = type))+
geom_boxplot()+
scale_fill_manual(values=colors1)+
geom_errorbar(data = summary.avg %>% filter(exp =="Exp.1"), colour="red", position = position_dodge(width = 5), width = 0, aes(ymin=look-ci, ymax=look+ci)) +
stat_summary(fun.y = mean, alpha = 0.8, geom = "point", shape=21, size=3, position = "dodge", colour = "red", fill = "red") +
ylab("Looking Time (s)") +
xlab("Test event") +
coord_cartesian(ylim = c(0, 65)) +
geom_point(alpha = 0.1)+
geom_line(alpha = 0.2, aes(group = subj))+
theme(legend.position="none")+
scale_x_discrete(labels = c("higher\nvalue", "lower\nvalue"))
# annotate("text", colour="red", x=1.5, y=63, size=5, label=c("*ß=0.354", "ß=0.168")) +
# facet_wrap(~exp, scales = "fixed", drop=TRUE)
# theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
risk1
Figure 2. Looking time towards test events in Experiment 2.
theme_set(theme_cowplot(font_size=20))
exp23.figure <- rbind(exp2.avg,exp3.avg) %>%
filter(agegroup == "older") %>%
mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
type=="shallow" | type =="lower" ~ "shallow"))
exp23.figure$cliff <- as.factor(exp23.figure$cliff)
exp23.figure$cliff <- relevel(exp23.figure$cliff, ref = "shallow")
exp23.figure$phase <- as.factor(exp23.figure$phase)
levels(exp23.figure$cliff)## [1] "shallow" "deep"levels(exp23.figure$phase) <- c("control", "test")
exp23.colors <- c(wes_palette("Royal2")[2], wes_palette("Royal2")[1])
exp2.figure <- ggplot(data = exp23.figure %>% filter(exp == "Exp.2"), aes(cliff, look, fill = cliff)) +
geom_boxplot(aes(alpha=phase))+
stat_summary(fun.y = mean, alpha = 0.8, geom = "point", shape=21, size=3, position = "dodge", colour = "red", fill = "red") +
geom_errorbar(data = summary.avg %>% filter(exp == "Exp.2"), colour="red", position = position_dodge(width = 5), width = 0, aes(ymin=look-ci, ymax=look+ci)) +
ylab("Looking Time (s)") +
xlab("Cliff Depth") +
coord_cartesian(ylim = c(0, 65)) +
geom_point(alpha = 0.1)+
geom_line(alpha = 0.2, aes(group = subj))+
facet_wrap(~phase, nrow=1)+
theme(legend.position="none")+
scale_fill_manual(values = exp23.colors)+
scale_alpha_discrete(range=c(0.4, 1))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
exp3.figure <- ggplot(data = exp23.figure %>% filter(exp == "Exp.3"), aes(cliff, look, fill = cliff)) +
geom_boxplot(aes(alpha=phase))+
stat_summary(fun.y = mean, alpha = 0.8, geom = "point", shape=21, size=3, position = "dodge", colour = "red", fill = "red") +
geom_errorbar(data = summary.avg %>% filter(exp == "Exp.3"), colour="red", position = position_dodge(width = 5), width = 0, aes(ymin=look-ci, ymax=look+ci)) +
ylab("Looking Time (s)") +
xlab("Cliff Depth") +
coord_cartesian(ylim = c(0, 65)) +
geom_point(alpha = 0.1)+
geom_line(alpha = 0.2, aes(group = subj))+
facet_wrap(~phase, nrow=1)+
theme(legend.position="none")+
scale_fill_manual(values = exp23.colors)+
scale_alpha_discrete(range=c(0.4, 1))+
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
exp2.figure + exp3.figure + plot_annotation(tag_levels = 'A')
Figure 4. Looking times from Experiments 2 (A, in-lab) and 3 (B, online) during the control events (lighter) and the test events (darker).
Experiment 1: Inferring value from risk
exp1.avg$type <- relevel(exp1.avg$type, ref = "higher")
exp1.0 <- lmer(loglook ~ 1 + (1|subj),
data = exp1.avg)
exp1.1 <- lmer(loglook ~ type + (1|subj),
data = exp1.avg)
# no influential observations
plot(influence(exp1.1, "subj"), which="cook",
cutoff=4/32, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
exp1.1.table <- gen.m(exp1.1)
exp1.1.ci <- gen.ci(exp1.1)[3:4,]
exp1.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
data = exp1.avg )
exp1.1.betas <- gen.beta(exp1.1.beta)
exp1.results <- cbind(exp1.1.table, exp1.1.betas,exp1.1.ci)
# effect size
exp1.cohensd <- lme.dscore(exp1.1,
data = exp1.avg %>% filter(subj != "S4_13"),
type = "lme4") %>% select(d) %>% as.numeric()Methods
Participants
Our final sample of participants included 32 thirteen-month-old infants (M=12.89 months, range=12.57-13.47, 17 female). Seven infants were excluded and replaced due to fussiness (3 infants) or inattentiveness during test trials (4 infants). Participants were recruited through a database of families who expressed interest in cognitive development research in the Boston area. Of the families in this database who chose to provide demographic information, 79.5% identified their children as White, 10.2% as Asian, 6.9% as Other, 2.5% as Black or African American, 0.4% as American Indian/Alaska Native, and 0.4% as Native Hawaiian/Pacific Islander; 90.3% as not Hispanic or Latino, 9.5% as Hispanic or Latino, and 0.2% as both. Most families in the database (90.4%) had at least one parent or legal guardian with a college diploma or higher. All data were collected at the Harvard Laboratory for Developmental Studies with procedures approved by the Committee on the Use of Human Subjects. We studied 13-month-old infants, rather than the 10-month-old infants tested in our past research [13]) because the younger infants lack experiences with walking and falling that may foster the development of these abilities. The sample size was chosen based on a simulation power analysis over the confirmatory analyses from 2 previous experiments with similar structure, conducted with 10-month-old infants: Experiments 1-2 from [13]), and we collected data until we attained our pre-specified N. The full pre-registration document, including full details about methods, sample size, hypotheses, and analysis plan, is available at https://osf.io/bfvdc/files/.
Data Coding and Analysis Strategy.
Infant looking times were coded online using XHAB (Pinto, 1995), and offline using Datavyu (Datavyu Team, 2014). All experimenters and coders were naive to the order of the test events and unable to see the video events (they relied on sound cues to start each trial). To check for exclusions and coding errors, all test trial data were re-coded in Datavyu and excluded if an infant looked away from a test event without ever having seen the agent jump, or if the trial ended too early or late (15 out of 320 total familiarization trials). We used these offline coded looking times for our final analyses. To assess the reliability of the data, (160 out of 320 trials) were re-coded in Datavyu by an additional researcher who was naive to test event order. Reliability was high, ICC=0.97, 95% CI [0.95, 0.98]. All decisions to include or exclude trials or participants from our analysis were made by researchers who did not know the order of events shown to that infant.
Infant looking times often are log-normally distributed, including in this dataset (log-likelihood of average looking times during test and control trials for Experiments 1-3 under normal distribution -2624.35, under lognormal distribution = -2456.26). Our pre-registered dependent measure therefore was the average looking time towards the higher- or lower-danger choice at test in log seconds. We report the values of unstandardized B coefficients and 95% confidence intervals in this unit, but our summary statistics and plots feature untransformed looking times for interpretability. We analyzed all looking times using mixed effects models (Bates et al., 2015) implemented in R (R Core Team, 2020). Analyses with repeated measures included a random intercept for participant identity; those conducted over multiple experiments included a random intercept for experiment. For every model, we checked for influential participants using Cook’s Distance (Nieuwenhuis et al., 2012) and excluded participants who exceeded the standard 4/n threshold, where n is the number of participants. The number of participants who met this criterion is listed in every model result; including or excluding them does not change the interpretation of any primary analysis (for results including all observations, see SOM). Data manipulation and plotting were conducted using tidyverse packages (Wickham et al., 2019). Cohen’s D derived from lme models were calculated using the EMAtools package (Kleiman, 2017). To enhance reproducibility, all results were written in R Markdown (Xie et al., 2018).
Results
Pre-registered results.
Infants looked longer when the agent chose the target achieved through the less dangerous action (Mlowervalue=24.6s, pooled standard error (SE)=1.14) than when the agent chose the target achieved through the more dangerous action (Mhighervalue=21.51s, SE=1.14 , 95% confidence interval (CI) over difference in log seconds [0.02,0.39], ß=0.34, t(31)=2.16, p=.039, two-tailed, Cohen’s d=0.79, no influential participants). As in the experiments of Liu et al. (2017) using closely similar methods, but presenting physically different actions on the two test trials, infants looked longer when this expected outcome did not occur.
Experiment 2: Minimizing Risk
exp2.test <- exp2.avg %>% filter(phase=="testavg")
exp2.info <- info(exp2.avg)
exp2.0 <- lmer(loglook ~ 1 + (1|subj),
data = exp2.test)
exp2.1 <- lmer(loglook ~ type + (1|subj),
data = exp2.test)
# id influential observations
plot(influence(exp2.1, "subj"), which="cook",
cutoff=4/30, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# one influential observation
exp2.1.cooks <- lmer(loglook ~ type + (1|subj),
data = exp2.test %>% filter(subj != "24-MR"))
exp2.1.table <- gen.m(exp2.1.cooks)
exp2.1.ci <- gen.ci(exp2.1.cooks)[3:4,]
exp2.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
data = exp2.test %>% filter(subj != "24-MR"))
exp2.1.betas <- gen.beta(exp2.1.beta)
exp2.results <- cbind(exp2.1.table, exp2.1.betas, exp2.1.ci)
# effect size
exp2.cohensd <- lme.dscore(exp2.1.cooks,
data = exp2.test %>% filter(subj != "24-MR"),
type = "lme4") %>% select(d) %>% as.numeric() *-1exp2.control <- exp2.avg %>% filter(phase=="control")
exp2.2 <- lmer(loglook ~ type + (1|subj),
data = exp2.control)
# id influential observations
plot(influence(exp2.2, "subj"), which="cook",
cutoff=4/30, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# two influential observation
exp2.cooks <- lmer(loglook ~ type + (1|subj),
data = exp2.control %>% filter(subj != "59-MR" & subj != "54-MR"))
exp2.table <- gen.m(exp2.cooks)
exp2.ci <- gen.ci(exp2.cooks)[3:4,]
exp2.beta <- lmer(scale(loglook) ~ type + (1|subj),
data = exp2.control %>% filter(subj != "59-MR" & subj != "54-MR"))
exp2.betas <- gen.beta(exp2.beta)
exp2.results2 <- cbind(exp2.table, exp2.betas, exp2.ci)
# effect size
exp2.control.cohensd <- lme.dscore(exp2.cooks,
data = exp2.control %>% filter(subj != "59-MR" & subj != "54-MR"),
type = "lme4") %>% select(d) %>% as.numeric() *-1exp2.control.test <- exp2.avg %>%
mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
type=="shallow" | type =="lower" ~ "shallow"))
exp2.control.test$type <- as.factor(exp2.control.test$type)
exp2.3 <- lmer(loglook ~ cliff * phase + (1|subj),
data = exp2.control.test)
plot(influence(exp2.3, "subj"), which="cook",
cutoff=4/30, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# no influential observations
exp2.3.table <- gen.m(exp2.3)
exp2.3.ci <- gen.ci(exp2.3)[3:6,]
exp2.3.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj),
data = exp2.control.test)
exp2.3.betas <- gen.beta(exp2.3.beta)
exp2.results3 <- cbind(exp2.3.table,exp2.3.betas,exp2.3.ci)
# effect size
exp2.interaction.cohensd <- lme.dscore(exp2.3,
data = exp2.control.test,
type = "lme4") %>% slice(3) %>% select(d) %>% as.numeric() *-1This study was originally pre-registered with a sample including both 10-month-old and 13-month-old infants. Because our investigation with 10-month-old infants is still ongoing, we deviate from our pre-registration by reporting only results from the older age group. Data from both age groups are open access at https://osf.io/kz7br/.
Methods
Participants
Our final sample of participants included 30 thirteen-month-old infants (M=12.89 months, range=12.53-13.5, 12 female). We chose this sample size using a simulation power analysis over the confirmatory analysis of data from a pilot study, as well as estimates of effect sizes of studies with similar displays and design (S. Liu et al., 2017; S. Liu & Spelke, 2017). Our pre-registration document is available at https://osf.io/efc3g/. We collected data until we attained our pre-specified N. Infants were excluded and replaced in the final sample due to fussiness that prevented study completion (3 infants), inattentiveness during test trials (2 infants), or interference from caregivers (2 infants).
Data Coding and Analysis
The data coding and analysis strategies were the same as in Experiment 1. Twenty-five out of 360 total familiarization, control, and test trials were excluded from the analysis based on inattentiveness or coding error. Half the test trials from the experiment (60/120 trials) were re-coded in Datavyu by an additional researcher who was naive to test event order. Reliability was high, ICC=1, 95% CI [1, 1].
Results
Infants looked longer when the agent, at test, chose to cross the deeper over the shallower trench (Mdeep=26.5s, SE=1.61; Mshallow=21.64s, SE=1.95; [0.03,0.43], ß=0.36, t(28)=2.33, p=.0135, one-tailed, d=0.88, excluding one influential participant).
In contrast, when infants’ attention was drawn to each trench by an attention-getting star that appeared in the path of the agent’s subsequent actions, infants looked longer at events near the shallow trench (Mdeep=12.73s, SE=1.11; Mshallow=16.02s, SE=2; [-0.31,-0.08], ß=-0.34, t(25.1)=-3.24, p=.003, two-tailed, d=-1.29,excluding 2 influential participants). Looking preferences between the control and test events differed significantly ([0.11,0.88], ß=0.75, t(84.74)=2.52, p=.013, two-tailed, d=0.55, no influential observations). See Figure 4A.
Experiment 3: Minimizing risk replication (no shattering)
exp3.control.test <- exp3.avg %>%
mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
type=="shallow" | type =="lower" ~ "shallow"))
exp3.avg.test <- exp3.avg %>% filter(phase=="testavg")
exp3.info <- info(exp3.avg)
exp3.0 <- lmer(loglook ~ 1 + (1|subj),
data = exp3.avg.test)
exp3.1 <- lmer(loglook ~ type + (1|subj),
data = exp3.avg.test)
# id influential observations
plot(influence(exp3.1, "subj"), which="cook",
cutoff=4/30, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# no influential observations
exp3.1.table <- gen.m(exp3.1)
exp3.1.ci <- gen.ci(exp3.1)[3:4,]
exp3.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
data = exp3.avg.test)
exp3.1.betas <- gen.beta(exp3.1.beta)
exp3.results <- cbind(exp3.1.table, exp3.1.betas, exp3.1.ci)
# effect size
exp3.cohensd <- lme.dscore(exp3.1,
data = exp3.avg.test,
type = "lme4") %>% select(d) %>% as.numeric() * 1exp3.control <- exp3.control.test %>% filter(phase=="control")
exp3.2 <- lmer(loglook ~ type + (1|subj),
data = exp3.control)
# id influential observations
plot(influence(exp3.2, "subj"), which="cook",
cutoff=4/42, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# two influential observation
exp3.2.cooks <- lmer(loglook ~ type + (1|subj),
data = exp3.control %>% filter(subj != "26" & subj != "28"))
exp3.2.table <- gen.m(exp3.2.cooks)
exp3.2.ci <- gen.ci(exp3.2.cooks)[3:4,]
exp3.2.beta <- lmer(scale(loglook) ~ type + (1|subj),
data = exp3.control %>% filter(subj != "26" & subj != "28"))
exp3.2.betas <- gen.beta(exp3.2.cooks)
exp3.results2 <- cbind(exp3.2.table, exp3.2.betas, exp3.2.ci)
# effect size
exp3.control.cohensd <- lme.dscore(exp3.2.cooks,
data = exp3.control %>% filter(subj != "26" & subj != "28"),
type = "lme4") %>% select(d) %>% as.numeric() *-1exp3.control.test$type <- as.factor(exp3.control.test$type)
exp3.3 <- lmer(loglook ~ cliff * phase + (1|subj),
data = exp3.control.test)
plot(influence(exp3.3, "subj"), which="cook",
cutoff=4/30, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# 1 influential observation
exp3.3.cooks <- lmer(loglook ~ cliff * phase + (1|subj),
data = exp3.control.test %>% filter(subj != "28"))
exp3.3.table <- gen.m(exp3.3.cooks)
exp3.3.ci <- gen.ci(exp3.3.cooks)[3:6,]
exp3.3.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj),
data = exp3.control.test %>% filter(subj != "28"))
exp3.3.betas <- gen.beta(exp3.3.beta)
exp3.results3 <- cbind(exp3.3.table,exp3.3.betas,exp3.3.ci)
# effect size
exp3.interaction.cohensd <- lme.dscore(exp3.3.cooks,
data = exp3.control.test %>% filter(subj != "28"),
type = "lme4") %>% slice(3) %>% select(d) %>% as.numeric() * -1# pairwise0 <- lsmeans(exp3.3.cooks, list(pairwise~cliff|phase))
#
# pairwise.beta <- lsmeans(exp3.3.beta, list(pairwise~cliff|phase))
#
# pairwise.beta.value <- pairwise.beta[[2]] %>% as.data.frame() %>% select(contrast, phase, estimate) %>%
# rename(beta = estimate)
#
# pairwise.CI <- confint(pairwise0[[2]]) %>% as.data.frame()
#
# pairwise.t.p <- pairwise0[[2]] %>% as.data.frame()
#
# within.exp3.almost <- full_join(pairwise.CI, pairwise.t.p) %>%
# rename(est = estimate,
# se = SE,
# lower = lower.CL,
# upper = upper.CL,
# t = t.ratio,
# p = p.value)
#
# within.exp3 <- full_join(pairwise.beta.value, within.exp3.almost)exp3.devices<- wide %>%
filter(exp == "Exp.3") %>%
tabyl(device)
exp3.highchair <- wide %>%
filter(exp == "Exp.3") %>%
tabyl(highchair)
exp3.qualratings <- wide %>%
filter(exp == "Exp.3") %>%
summarise(vquality = mean(video_quality, na.rm=TRUE),
vquality.sd = sd(video_quality, na.rm=TRUE),
aquality = mean(audio_quality, na.rm=TRUE),
aquality.sd = sd(audio_quality, na.rm=TRUE))Methods
Participants
Our final sample included 42 twelve- to fifteen-month-old infants (M=13.95 months, range=12.29-15.67, 24 female): a widened age range that enabled more rapid testing of participants, who were recruited both from our lab database, also through a cross-institution platform for recruitment for developmental cognitive science (https://childrenhelpingscience.com/). Our preregistered target sample size of 40 was determined based on a simulation power analysis over infants’ looking preferences towards the test events from Experiment 2; our stopping rule was to stop recruiting as soon as we reached our target N, but to finish collecting data if we over-recruited. Thus, our final sample was N=42. A further 6 infants were excluded from the study (3 due to technical issues, 2 due to inattentiveness and 1 due to interference from the caregiver). Our pre-registration document is available at https://osf.io/96qsf/.
Procedure
Whereas Experiments 1 and 2 were conducted in a quiet, dark room in a lab setting, Experiment 3 was conducted over Zoom video conferencing, in infants’ homes, due to the COVID-19 pandemic, following procedures approved by the Committee on the Use of Human Subjects at Harvard University. We used materials developed by the Stanford Social Learning Lab (Social Learning Lab, 2020) to introduce caregivers to the online testing setup and to ask for verbal consent. Caregivers also provided written consent prior to the study session. Infants sat in a high chair (25 out of 42 participants) or their caregivers’ laps (17/42), depending on caregiver preferences, and watched the displays on a tablet (8/42) or a laptop computer (34/42). We asked caregivers, both before and during the study, to minimize distractions (pets, people walking by, and distracting objects) during the study session.
Before the experiment, infants saw a calibration video where their attention was drawn to the four corners of the screen, as well as the center of the screen. To maximize the quality of the events seen by infants, we shared our stimuli with caregivers through YouTube playlists, controlled the caregiver’s screen using Zoom’s remote control feature, and coded infants’ looking times during the study using jHab (Casstevens, 2007). Caregivers rated the quality of the audio and video on a 5-point Likert scale (1 = very poor; 5 = very good), giving high ratings, on average, for both (video: M=4.88, SD=0.33; audio: M=4.85, SD=0.36). After the session, we double checked for trial exclusions and generated the final data from the recording of the session video using Datavyu (Datavyu Team, 2014). As before, experimenters only had access to the video feed of infants’ faces (and not the displays) during the experiment, and therefore were unaware of the order of test events. To allow caregivers to attend to safety issues at home, we did not ask them to close their eyes, and instead instructed them to refrain from directing their infants’ attention toward or away from the screen. Our full online testing protocol is described in the SOM.
Data Coding and Analysis
The data coding and analysis strategy was identical to Experiment 2. Fifty-three out of NA total trials (including familiarization, test, and control trials) were excluded from analysis because of inattentiveness, distractions at home (e.g. pet noises, people walking by), technical issues and coding errors. The proportion of excluded trials (10.52%) was higher than what we observed in the lab in Experiment 2 (6.94%), due to distractions in the home environment, the smaller size of the screen displaying the videos at home, and the lower or more variable quality of the video feeds of the infants’ faces (which led to trial mis-timings). As in Experiments 1-2, 50% of the test trials were recoded by an additional naive coder (84 of 168 test trials). Interrater reliability was high, ICC=0.96, 95% CI [0.93, 0.97].
Results
Pre-registered results
We fully replicated the two key results from Experiment 2. Infants looked longer at test when the agent chose to jump the deeper trench (Mdeep=22.35s, SE=1.26; Mshallow=17.55s, SE=1; [0.09,0.41], ß=0.47, t(41)=3.06, p=.002, one-tailed, d=-0.96, no influential participants). Infants’ looking preferences between the control events and the test events significantly differed from each other ([0.13,0.75], ß=0.74, t(105.17)=2.76, p=.007, two-tailed, d=0.54, excluding 1 influential participant).
Exploratory results
During the control events, infants showed a numerical but non-significant preference for the event in which the inanimate object appeared over the shallower trench (Mdeep=12.09s, SE=1.27; Mshallow=13.9s, SE=1.48; [-0.42,0.07], ß=-0.17, t(64)=-1.39, p=.171, two-tailed, d=-0.35, excluding 2 influential participants). See Figure 4A.
Experiment 4, Studies 1-3 (10mo infants)
Study 1: Inferring value from risk (10-month-old infants)
exp1b.0 <- lmer(loglook ~ 1 + (1|subj),
data = exp1b.avg)
exp1b.1 <- lmer(loglook ~ type + (1|subj),
data = exp1b.avg)
# id influential observations
plot(influence(exp1b.1, "subj"), which="cook",
cutoff=4/32, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# one influential observation, exclude
exp1b.1.cooks <- lmer(loglook ~ type + (1|subj),
data = filter(exp1b.avg, subj != "S5_25"))
exp1b.1.table <- gen.m(exp1b.1.cooks)
exp1b.1.ci <- gen.ci(exp1b.1.cooks)[3:4,]
exp1b.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
data = filter(exp1b.avg, subj != "S5_25"))
exp1b.1.betas <- gen.beta(exp1b.1.beta)
exp1b.results <- cbind(exp1b.1.table, exp1b.1.betas,exp1b.1.ci)
exp1b.cohensd <- lme.dscore(exp1b.1.cooks,
data = filter(exp1b.avg, subj != "S5_25"),
type = "lme4") %>% select(d) %>% as.numeric() Comparing older and younger infants
exp1.1013 <- long.avg %>%
filter(exp == "Exp.1" | exp == "Exp.1b") %>%
mutate(agegroup = as.factor(case_when(agem < 12 ~ "younger",
agem > 12 ~ "older")))
exp1.1013$type <- relevel(exp1.1013$type, ref = "higher")
exp1.1013.1 <- lmer(loglook ~ type * agegroup + (1|subj),
data = exp1.1013)
# id influential observations
plot(influence(exp1.1013.1, "subj"), which="cook",
cutoff=4/64, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# one influential observation, exclude
exp1.1013.1.cooks <- lmer(loglook ~ type * agegroup + (1|subj),
data = exp1.1013 %>% filter(subj != "S5_25"))
exp1.1013.1.table <- gen.m(exp1.1013.1.cooks)
exp1.1013.1.ci <- gen.ci(exp1.1013.1.cooks)[3:4,]
exp1.1013.1.beta <- lmer(scale(loglook) ~ type * agegroup + (1|subj),
data = exp1.1013 %>% filter(subj != "S5_25"))
exp1.1013.1.betas <- gen.beta(exp1.1013.1.beta)
exp1.1013.results <- cbind(exp1.1013.1.table, exp1.1013.1.betas,exp1.1013.1.ci)
exp1.1013.cohensd <- lme.dscore(exp1.1013.1.cooks,
data = filter(exp1.1013, subj != "S5_25"),
type = "lme4") %>% select(d) %>% slice(3) %>% as.numeric() Study 2: Minimizing danger
exp2b.test <- exp2b.avg %>% filter(phase=="testavg")
exp2b.info <- info(exp2b.test)
exp2b.0 <- lmer(loglook ~ 1 + (1|subj),
data = exp2b.test)
exp2b.1 <- lmer(loglook ~ type + (1|subj),
data = exp2b.test)
summary(exp2b.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ type + (1 | subj)
## Data: exp2b.test
##
## REML criterion at convergence: 116
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9659 -0.6026 0.0678 0.5726 1.7083
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.244 0.494
## Residual 0.210 0.458
## Number of obs: 60, groups: subj, 30
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.997 0.123 45.005 24.4 <2e-16 ***
## typelower -0.202 0.118 29.000 -1.7 0.099 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typelower -0.481# id influential observations
plot(influence(exp2b.1, "subj"), which="cook",
cutoff=4/30, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# no influential observations
exp2b.1.cooks <- lmer(loglook ~ type + (1|subj),
data = exp2b.test %>% filter(subj != "03-MR"))
exp2b.1.table <- gen.m(exp2b.1.cooks)
exp2b.1.ci <- gen.ci(exp2b.1.cooks)[3:4,]
exp2b.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
data = exp2b.test %>% filter(subj != "03-MR"))
exp2b.1.betas <- gen.beta(exp2b.1.beta)
exp2b.1.results <- cbind(exp2b.1.table, exp2b.1.betas, exp2b.1.ci)
exp2b.test.cohensd <- lme.dscore(exp2b.1.cooks,
data = exp2b.test %>% filter(subj != "03-MR"),
type = "lme4") %>% select(d) %>% as.numeric() exp2b.control <- exp2b.avg %>% filter(phase=="control")
exp2b.2 <- lmer(loglook ~ type + (1|subj),
data = exp2b.control)
# id influential observations
plot(influence(exp2b.2, "subj"), which="cook",
cutoff=4/30, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
# one influential observation
exp2b.2.cooks <- lmer(loglook ~ type + (1|subj),
data = exp2b.control %>% filter(subj != "21-MR"))
exp2b.2.table <- gen.m(exp2b.2.cooks)
exp2b.2.ci <- gen.ci(exp2b.2.cooks)[3:4,]
exp2b.2.beta <- lmer(scale(loglook) ~ type + (1|subj),
data = exp2b.control %>% filter(subj != "21-MR"))
exp2b.2.betas <- gen.beta(exp2b.2.beta)
exp2b.results2 <- cbind(exp2b.2.table, exp2b.2.betas, exp2b.2.ci)
exp2b.control.cohensd <- lme.dscore(exp2b.2.cooks,
data = exp2b.control %>% filter(subj != "21-MR"),
type = "lme4") %>% select(d) %>% as.numeric() exp2b.control.test <- exp2b.avg %>%
mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
type=="shallow" | type =="lower" ~ "shallow"))
# summary.mr <- summarySEwithin(data = exp2b.control.test, measurevar = "look", withinvars = c("cliff", "type", "phase"), idvar="subj")
exp2b.control.test$type <- as.factor(exp2b.control.test$type)
exp2b.3 <- lmer(loglook ~ cliff * phase + (1|subj),
data = exp2b.control.test)
plot(influence(exp2b.3, "subj"), which="cook",
cutoff=4/30, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
exp2b.3.table <- gen.m(exp2b.3)
exp2b.3.ci <- gen.ci(exp2b.3)[3:6,]
exp2b.3.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj),
data = exp2b.control.test)
exp2b.3.betas <- gen.beta(exp2b.3.beta)
exp2b.results3 <- cbind(exp2b.3.table,exp2b.3.betas,exp2b.3.ci)
exp2b.interaction.cohensd <- lme.dscore(exp2b.3,
data = exp2b.control.test,
type = "lme4") %>% select(d) %>% slice(3) %>% as.numeric() Study 3: Minimizing danger (online, no shattering)
exp3b.devices<- wide %>%
filter(exp == "Exp.3b") %>%
tabyl(device)
exp3b.highchair <- wide %>%
filter(exp == "Exp.3b") %>%
tabyl(highchair)
exp3b.qualratings <- wide %>%
filter(exp == "Exp.3b") %>%
summarise(vquality = mean(video_quality, na.rm=TRUE),
vquality.sd = sd(video_quality, na.rm=TRUE),
aquality = mean(audio_quality, na.rm=TRUE),
aquality.sd = sd(audio_quality, na.rm=TRUE))exp3b.test <- exp3b.avg %>% filter(phase == "testavg")
exp3b.avg$type <- relevel(exp3b.avg$type, ref = "lower")
exp3b.1 <- lmer(data = exp3b.test,
formula = loglook ~ type + (1|subj))
summary(exp3b.1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ type + (1 | subj)
## Data: exp3b.test
##
## REML criterion at convergence: 136
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8600 -0.6556 -0.0803 0.7117 2.2508
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.0594 0.244
## Residual 0.2513 0.501
## Number of obs: 80, groups: subj, 40
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6459 0.0881 75.2501 30.02 <2e-16 ***
## typelower 0.0845 0.1121 39.0000 0.75 0.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typelower -0.636# 1 influential subject found
plot(influence(exp3b.1, "subj"), which="cook",
cutoff=4/40, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
exp3b.1.cooks <- lmer(data = exp3b.test %>% filter(subj != "10m_33"),
formula = loglook ~ type + (1|subj))
summary(exp3b.1.cooks)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ type + (1 | subj)
## Data: exp3b.test %>% filter(subj != "10m_33")
##
## REML criterion at convergence: 126
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0703 -0.6539 -0.0543 0.7160 1.7391
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.0868 0.295
## Residual 0.2041 0.452
## Number of obs: 78, groups: subj, 39
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6154 0.0864 69.7853 30.28 <2e-16 ***
## typelower 0.1357 0.1023 38.0000 1.33 0.19
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## typelower -0.592exp3b.1.table <- gen.m(exp3b.1.cooks)
exp3b.1.ci <- gen.ci(exp3b.1.cooks)[3:4,]
exp3b.1.beta <- lmer(scale(loglook) ~type + (1|subj),
data = exp3b.test %>% filter(subj != "10m_33"))
exp3b.1.betas <- gen.beta(exp3b.1.beta)
exp3b.1.results <- cbind(exp3b.1.table, exp3b.1.betas, exp3b.1.ci)
exp3b.test.cohensd <- lme.dscore(exp3b.1.cooks,
data = exp3b.test %>% filter(subj != "10m_33"),
type = "lme4") %>% select(d) %>% as.numeric() exp3b.control.test <- exp3b.avg %>%
mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
type=="shallow" | type =="lower" ~ "shallow"))
exp3b.control.test$cliff <- as.factor(exp3b.control.test$cliff)
exp3b.2 <- lmer(data = exp3b.control.test,
formula = loglook ~ cliff * phase + (1|subj))
summary(exp3b.2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ cliff * phase + (1 | subj)
## Data: exp3b.control.test
##
## REML criterion at convergence: 263
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8666 -0.7086 -0.0829 0.6966 2.3647
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.049 0.221
## Residual 0.267 0.517
## Number of obs: 154, groups: subj, 40
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.3423 0.0922 142.0665 25.39 <2e-16 ***
## cliffshallow 0.0377 0.1203 112.0503 0.31 0.754
## phasetestavg 0.3036 0.1181 112.8277 2.57 0.011 *
## cliffshallow:phasetestavg 0.0468 0.1668 111.5145 0.28 0.780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) clffsh phstst
## cliffshallw -0.652
## phasetestvg -0.668 0.509
## clffshllw:p 0.470 -0.721 -0.706# no influential subjects detected
plot(influence(exp3b.2, "subj"), which="cook",
cutoff=4/40, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
exp3b.2.table <- gen.m(exp3b.2)
exp3b.2.ci <- gen.ci(exp3b.2)[3:6,]
exp3b.2.beta <- lmer(scale(loglook) ~cliff * phase + (1|subj),
data = exp3b.control.test)
exp3b.2.betas <- gen.beta(exp3b.2.beta)
exp3b.2.results <- cbind(exp3b.2.table, exp3b.2.betas, exp3b.2.ci)
exp3b.interaction.cohensd <- lme.dscore(exp3b.2,
data = exp3b.control.test,
type = "lme4") %>% select(d) %>% slice(3) %>% as.numeric() exp3b.control <- exp3b.avg %>%
filter(phase=="control") %>%
mutate(cliff = type)
exp3b.3 <- lmer(data = exp3b.control,
formula = loglook ~ cliff + (1|subj))
summary(exp3b.3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ cliff + (1 | subj)
## Data: exp3b.control
##
## REML criterion at convergence: 129
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8134 -0.6761 -0.0894 0.7408 2.0842
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.0495 0.223
## Residual 0.2718 0.521
## Number of obs: 74, groups: subj, 38
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.3493 0.0932 70.5281 25.22 <2e-16 ***
## cliffshallow 0.0282 0.1215 36.1603 0.23 0.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## cliffshallw -0.652plot(influence(exp3b.3, "subj"), which="cook",
cutoff=4/40, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
exp3b.3.table <- gen.m(exp3b.3)
exp3b.3.ci <- gen.ci(exp3b.3)[3:4,]
exp3b.3.beta <- lmer(data = exp3b.control,
formula = scale(loglook) ~ cliff + (1|subj))
exp3b.3.betas <- gen.beta(exp3b.3.beta)
exp3b.3.results <- cbind(exp3b.3.table, exp3b.3.betas, exp3b.3.ci)
exp3b.control.cohensd <- lme.dscore(exp3b.3,
data = exp3b.control.test,
type = "lme4") %>%
select(d) %>% as.numeric() Comparing Studies 2-3 and Experiments 2-3
exp23.1013.avg <- rbind(exp3.avg, exp2.avg, exp2b.avg, exp3b.avg) %>%
mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
type=="shallow" | type =="lower" ~ "shallow"))
pooling.exp23 <- lmer(loglook ~ cliff * phase * agegroup + (1|subj) + (1|exp), data = exp23.1013.avg)
summary(pooling.exp23)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ cliff * phase * agegroup + (1 | subj) + (1 | exp)
## Data: exp23.1013.avg
##
## REML criterion at convergence: 973
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.697 -0.642 -0.032 0.623 2.376
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.06820 0.2612
## exp (Intercept) 0.00429 0.0655
## Residual 0.28228 0.5313
## Number of obs: 546, groups: subj, 142; exp, 4
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.36691 0.08763 6.64020
## cliffshallow 0.16374 0.09404 399.12203
## phasetestavg 0.67389 0.09215 408.33650
## agegroupyounger -0.00772 0.12270 6.38565
## cliffshallow:phasetestavg -0.42765 0.12917 398.14107
## cliffshallow:agegroupyounger -0.08539 0.13148 399.09339
## phasetestavg:agegroupyounger -0.23143 0.12948 404.54420
## cliffshallow:phasetestavg:agegroupyounger 0.31119 0.18219 398.10690
## t value Pr(>|t|)
## (Intercept) 27.01 4.9e-08 ***
## cliffshallow 1.74 0.082 .
## phasetestavg 7.31 1.4e-12 ***
## agegroupyounger -0.06 0.952
## cliffshallow:phasetestavg -3.31 0.001 **
## cliffshallow:agegroupyounger -0.65 0.516
## phasetestavg:agegroupyounger -1.79 0.075 .
## cliffshallow:phasetestavg:agegroupyounger 1.71 0.088 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) clffsh phstst aggrpy clffshllw:p clffshllw:g phsts:
## cliffshallw -0.546
## phasetestvg -0.565 0.519
## agegropyngr -0.714 0.390 0.404
## clffshllw:p 0.398 -0.728 -0.707 -0.284
## clffshllw:g 0.391 -0.715 -0.371 -0.541 0.521
## phststvg:gg 0.402 -0.370 -0.712 -0.554 0.503 0.512
## clffshllw:: -0.282 0.516 0.502 0.390 -0.709 -0.722 -0.707plot(allEffects(pooling.exp23))
lsmeans(pooling.exp23, pairwise ~ cliff * phase * agegroup)## $lsmeans
## cliff phase agegroup lsmean SE df lower.CL upper.CL
## deep control older 2.37 0.088 6.6 2.16 2.6
## shallow control older 2.53 0.087 6.4 2.32 2.7
## deep testavg older 3.04 0.084 5.6 2.83 3.3
## shallow testavg older 2.78 0.084 5.6 2.57 3.0
## deep control younger 2.36 0.086 6.1 2.15 2.6
## shallow control younger 2.44 0.086 6.1 2.23 2.6
## deep testavg younger 2.80 0.085 5.8 2.59 3.0
## shallow testavg younger 2.76 0.085 5.8 2.55 3.0
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio
## deep control older - shallow control older -0.16 0.094 401 -1.700
## deep control older - deep testavg older -0.67 0.092 410 -7.300
## deep control older - shallow testavg older -0.41 0.092 410 -4.400
## deep control older - deep control younger 0.01 0.123 6 0.100
## deep control older - shallow control younger -0.07 0.123 6 -0.600
## deep control older - deep testavg younger -0.43 0.122 6 -3.600
## deep control older - shallow testavg younger -0.40 0.122 6 -3.200
## shallow control older - deep testavg older -0.51 0.091 408 -5.600
## shallow control older - shallow testavg older -0.25 0.091 408 -2.700
## shallow control older - deep control younger 0.17 0.122 6 1.400
## shallow control older - shallow control younger 0.09 0.122 6 0.800
## shallow control older - deep testavg younger -0.27 0.121 6 -2.200
## shallow control older - shallow testavg younger -0.23 0.121 6 -1.900
## deep testavg older - shallow testavg older 0.26 0.089 399 3.000
## deep testavg older - deep control younger 0.68 0.120 6 5.700
## deep testavg older - shallow control younger 0.60 0.120 6 5.000
## deep testavg older - deep testavg younger 0.24 0.120 6 2.000
## deep testavg older - shallow testavg younger 0.28 0.120 6 2.300
## shallow testavg older - deep control younger 0.42 0.120 6 3.500
## shallow testavg older - shallow control younger 0.34 0.120 6 2.800
## shallow testavg older - deep testavg younger -0.02 0.120 6 -0.200
## shallow testavg older - shallow testavg younger 0.01 0.120 6 0.100
## deep control younger - shallow control younger -0.08 0.092 401 -0.900
## deep control younger - deep testavg younger -0.44 0.091 403 -4.900
## deep control younger - shallow testavg younger -0.40 0.091 403 -4.400
## shallow control younger - deep testavg younger -0.36 0.091 403 -4.000
## shallow control younger - shallow testavg younger -0.33 0.091 403 -3.600
## deep testavg younger - shallow testavg younger 0.04 0.090 399 0.400
## p.value
## 0.6600
## <.0001
## <.0001
## 1.0000
## 1.0000
## 0.1100
## 0.1500
## <.0001
## 0.1300
## 0.8300
## 0.9900
## 0.4400
## 0.5800
## 0.0600
## 0.0100
## 0.0300
## 0.5400
## 0.4100
## 0.1200
## 0.2400
## 1.0000
## 1.0000
## 0.9900
## <.0001
## <.0001
## <.0001
## 0.0100
## 1.0000
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimatesplot(influence(pooling.exp23, "subj"), which="cook",
cutoff=4/142, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
pooling.exp23.cooks <- lmer(loglook ~ cliff * phase * agegroup + (1|subj) + (1|exp), data = exp23.1013.avg %>% filter(subj != "28"))
summary(pooling.exp23.cooks)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ cliff * phase * agegroup + (1 | subj) + (1 | exp)
## Data: exp23.1013.avg %>% filter(subj != "28")
##
## REML criterion at convergence: 952
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9448 -0.6493 -0.0278 0.6330 2.4232
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.07202 0.2684
## exp (Intercept) 0.00405 0.0636
## Residual 0.27168 0.5212
## Number of obs: 542, groups: subj, 141; exp, 4
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.35593 0.08675 6.69366
## cliffshallow 0.20654 0.09299 396.05008
## phasetestavg 0.67616 0.09112 405.16215
## agegroupyounger 0.00271 0.12115 6.36409
## cliffshallow:phasetestavg -0.46520 0.12767 395.09467
## cliffshallow:agegroupyounger -0.12780 0.12952 396.00722
## phasetestavg:agegroupyounger -0.23331 0.12755 401.42519
## cliffshallow:phasetestavg:agegroupyounger 0.34835 0.17942 395.05325
## t value Pr(>|t|)
## (Intercept) 27.16 4.2e-08 ***
## cliffshallow 2.22 0.0269 *
## phasetestavg 7.42 6.9e-13 ***
## agegroupyounger 0.02 0.9829
## cliffshallow:phasetestavg -3.64 0.0003 ***
## cliffshallow:agegroupyounger -0.99 0.3244
## phasetestavg:agegroupyounger -1.83 0.0681 .
## cliffshallow:phasetestavg:agegroupyounger 1.94 0.0529 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) clffsh phstst aggrpy clffshllw:p clffshllw:g phsts:
## cliffshallw -0.546
## phasetestvg -0.565 0.519
## agegropyngr -0.716 0.391 0.405
## clffshllw:p 0.398 -0.728 -0.707 -0.285
## clffshllw:g 0.392 -0.718 -0.373 -0.540 0.523
## phststvg:gg 0.404 -0.371 -0.714 -0.553 0.505 0.512
## clffshllw:: -0.283 0.518 0.503 0.390 -0.712 -0.722 -0.707pooling.exp23.table <- gen.m(pooling.exp23.cooks)
pooling.exp23.ci <- gen.ci(pooling.exp23.cooks)[3:10,]
pooling.exp23.betas <- gen.beta(pooling.exp23.cooks)
pooling.exp23.results <- cbind(pooling.exp23.table, pooling.exp23.betas, pooling.exp23.ci)
pooling.exp23.cohensd <- lme.dscore(pooling.exp23.cooks,
data = exp23.1013.avg %>% filter(subj != "28"),
type = "lme4") %>% slice(7) %>%
select(d) %>% as.numeric()exp23.1013.test <- exp23.1013.avg %>% filter(phase=="testavg")
exp23.1013.control <- exp23.1013.avg %>% filter(phase=="control")
pooling.exp23.test <- lmer(loglook ~ cliff * agegroup + (1|subj) + (1|exp), data = exp23.1013.test)
plot(influence(pooling.exp23.test, "subj"), which="cook",
cutoff=4/142, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
pooling.exp23.test.cooks <- lmer(loglook ~ cliff * agegroup + (1|subj) + (1|exp), data = exp23.1013.test %>% filter(subj != "10m_33"))
pooling.exp23.test.table <- gen.m(pooling.exp23.test.cooks)
pooling.exp23.test.ci <- gen.ci(pooling.exp23.test.cooks)[4:7,]
pooling.exp23.test.betas <- gen.beta(pooling.exp23.test.cooks)
pooling.exp23.test.results <- cbind(pooling.exp23.test.table, pooling.exp23.test.betas, pooling.exp23.test.ci)
pooling.exp23.test.cohensd <- lme.dscore(pooling.exp23.test.cooks,
data = exp23.1013.test %>% filter(subj != "10_33"),
type = "lme4") %>% slice(3) %>%
select(d) %>% as.numeric()
pooling.exp23.control <- lmer(loglook ~ cliff * agegroup + (1|subj) + (1|exp), data = exp23.1013.control)
plot(influence(pooling.exp23.control, "subj"), which="cook",
cutoff=4/142, sort=TRUE,
xlab="Cook´s Distance",
ylab="Subject ID")
pooling.exp23.control.cooks <- lmer(loglook ~ cliff * agegroup + (1|subj) + (1|exp), data = exp23.1013.control %>% filter(subj != "28" & subj != "26"))
pooling.exp23.control.table <- gen.m(pooling.exp23.control.cooks)
pooling.exp23.control.ci <- gen.ci(pooling.exp23.control.cooks)[4:7,]
pooling.exp23.control.betas <- gen.beta(pooling.exp23.control.cooks)
pooling.exp23.control.results <- cbind(pooling.exp23.control.table, pooling.exp23.control.betas, pooling.exp23.control.ci)
pooling.exp23.control.cohensd <- lme.dscore(pooling.exp23.control.cooks,
data = exp23.1013.control %>% filter(subj != "28" & subj != "26"),
type = "lme4") %>% slice(3) %>%
select(d) %>% as.numeric()In Experiment 4, we investigated the developmental origins of the capacity to reason about danger by testing infants under one year of age, using the respective methods of Experiments 1-3. We will reference these samples as Experiment 4, Studies 1, 2, and 3. All 3 studies focused on 10-month-olds because of their previous success in reasoning about the physical costs of actions (e.g. in [redacted]).
Methods
Participants
Our final sample included a grand total of 102 10-month-old infants. Studies 1-2 were conducted in the lab, and our final sample included 32 infants in Study 1 (M=10.13 months, range=9.6-10.63, 15 female; an additional 6 infants tested and excluded from the final sample), and 30 infants in Study 2 (M=9.95 months, range=8.97-10.47, 17 female; an additional 2 infants tested and excluded). In Study 3, we collected an online sample of 40 (M=(M=10.24 months, range=9.53-11.06, 20 female, xxxx excluded). In the online sample, infants sat in a high chair (13 out of 40 participants) or their caregivers’ laps (27/40), depending on caregiver preferences, and watched the displays on a tablet (12/40) or a laptop computer (28/40). Caregivers gave high ratings for both the video quality (M=4.88, SD=0.33) and audio quality (M=4.86, SD=0.34). All three of these studies were pre-registered (Study 1: https://osf.io/uh8ns/; Study 2: https://osf.io/kx928/, Study 3: https://osf.io/48j9v/) Data reliability. As in Experiments 1-3, the reliability of the looking time data in Experiment 4 were high (Study 1: ICC=0.995, 95% CI [0.991, 0.997]; Study 2: ICC=0.999, 95% CI [0.998, 0.999]; Study 3:ICC=0.909, 95% CI [0.859, 0.942]).
Results
Inferring value from danger (Study 1)
When we tested 10-month-old infants using identical protocols as reported in Experiment 1, these younger infants did not show a statistically significant looking preference between the test events, (Mhighervalue=19.15s, Mlowervalue= 19.51, pooled SE=1.22, [-0.121,0.301], ß=0.168, B=0.09, SE=0.106, p=.202, one-tailed d=0.31, removing 1 influential participant). Comparing the data from Experiment 1 and Experiment 4, Study 1, 10- and 13-month-old infants did not significantly differ in their looking preferences in this task, [0.009,0.4], ß=-0.202, B=-0.115, SE=0.142, p=.422, two-tailed, d=-0.21, no influential participants.
Avoiding danger (Studies 2-3)
When we tested 10-month-old infants in identical protocols as Experiment 2, ten-month-old looked longer at test when the agent chose the deeper the shallower trench (Mdeeper=24.97s, Mshallower= 20.31, pooled SE=1.51, [-0.472,-0.047], ß=-0.386, B=-0.26, SE=0.107, p=.011, one-tailed d=0.92, removing 1 influential participant). During control events, 10-month-old infants did not show a significant looking preference (Mdeeper=12.74s, Mshallower= 14.68, pooled SE=1.97, [-0.174,0.301], ß=0.109, B=0.064, SE=0.119, p=.598, two-tailed d=-0.2, excluding 1 influential participant). In contrast to the data from older infants, these two patterns of looking preference did not differ from each other, [-0.728,0.073], ß=-0.483, B=-0.327, SE=0.205, p=.115, two-tailed, d=0.05no influential participants.
Notably, we did not replicate the results of Study 2 when we ran an additional online sample of infants: In Study 3, 10-month-old infants did not show a looking preference during the test events (Mdeeper=16.03s, Mshallower= 18.22, pooled SE=1.26, [-0.067,0.339], ß=0.251, B=0.136, SE=0.102, p=.0965, one-tailed d=0.43, excluding 1 influential participant), or the control events (Mdeeper=12.74s, Mshallower= 12.3, pooled SE=1.24, [-0.213,0.269], ß=0.05, B=0.028, SE=0.121, p=.818, two-tailed d=-0.13, no influential participants), and their looking preferences did not differ across the two phases of the experiment, [-0.28,0.37], ß=0.08, B=0.05, SE=0.17, p=.78, two-tailed, d=0.05, no influential observations.
Pooling data across older and younger infants tested in Experiments 2-3, and Experiment 4, Studies 2-3, we found a marginal 3-way interaction between cliff depth (shallow vs deep), phase of experiment (control vs test), and age group (infants younger than 1y vs older than 1y), [-0.482,0.016], ß=0.348, B=0.348, SE=0.179, p=.053, two-tailed, d=0.2, 1 influential participant.
This interaction appeared to be driven by differences in younger and older infants’ responses to the test events: we found a significant interaction between age group (younger vs older than 1y) and cliff depth (shallow vs deep) for the test events, [0.053,0.453], ß=0.253, B=0.253, SE=0.103, p=.015, two-tailed, d=0.36, excluding one influential participant, but not the control events, [-0.362,0.119], ß=-0.121, B=-0.121, SE=0.123, p=.326, two-tailed, d=-0.18excluding two influential participants.
Thus, in a large and sufficiently powered sample (N=142), infants younger and infants older than 1 year of age differed in their pattern of looking responses to events where agents choose more vs less dangerous actions, but did not differ when their attention was simply drawn to the physical trenches where these actions occurred.
Supplemental Materials
# is a lognormal transformation justified given the distribution of looks?
fig.S1 <- ggplot(data = risk.avg, aes(look, fill=exp))
fig.S1 +
geom_density(alpha = 0.5)+
# geom_text(aes(experiment))+
theme_cowplot(20)+
# facet_wrap(~exp)+
xlab("Looking Time (s)")
# scale_fill_brewer(palette="Set2")Figure S1. Density plot of looking times during test for Experiment 1 and from test events and control events for Experiments 2-3. Maximum-likelihood fitting revealed that the lognormal distribution (log likelihood=-2456.26) provides a better fit to these data than the normal distribution (log likelihood=-2624.35).
fam <- wide %>%
filter(cost=="Risk")%>%
gather(trial, look, fam1:test4) %>%
mutate(trial_n = parse_number(trial)) %>%
mutate(trial_type = str_extract(trial, "[a-z]+"))
fam$trial_type <- as.factor(fam$trial_type)
fam$trial_n <- as.factor(fam$trial_n)
fam$look <- as.numeric(as.character(fam$look))
famplot <- ggplot(data = fam, aes(trial_n, look, fill=trial_type))
famplot + geom_boxplot() +
facet_wrap(~exp+trial_type, nrow=2)+
xlab("Trial N")+
stat_summary(fun.data =mean_cl_boot, geom="errorbar",width=0.1)+
stat_summary(fun.y=mean,geom="point",shape=5)+
ylab("Looking time (s)")+
theme_cowplot(20)
Figure S2. Boxplots of looking times during familiarization and test across Experiments (total N=206). Error bars represent bootstrapped 95% confidence intervals around the mean.
Including influential observations (reviewer suggestion)
Below, we report the results from our pre-registered analyses including all observations, rather than excluding influential observations. We report the analysis for Exp 2 and 3 only because no influential observations were observed in the confirmatory analysis of Experiment 1.
exp2.everyone.test.table <- gen.m(exp2.1)
exp2.everyone.test.ci <- gen.ci(exp2.1)[3:4,]
exp2.everyone.test.beta <- lmer(scale(loglook)~ type + (1 | subj),
data=exp2.test)
exp2.everyone.test.betas <- gen.beta(exp2.everyone.test.beta)
exp2.everyone.test.results <- cbind(exp2.everyone.test.betas,exp2.everyone.test.table,exp2.everyone.test.ci)
exp2.everyone.pre.table <- gen.m(exp2.2)
exp2.everyone.pre.ci <- gen.ci(exp2.2)[3:4,]
exp2.everyone.pre.beta <- lmer(scale(loglook) ~ type + (1 | subj),
data=exp2.control)
exp2.everyone.pre.betas <- gen.beta(exp2.everyone.pre.beta)
exp2.everyone.pre.results <- cbind(exp2.everyone.pre.betas,exp2.everyone.pre.table,exp2.everyone.pre.ci)
exp2.everyone.prevstest.table <- gen.m(exp2.3)
exp2.everyone.prevstest.ci <- gen.ci(exp2.3)[3:6,]
exp2.everyone.prevstest.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj), data = exp2.control.test)
exp2.everyone.prevstest.betas <- gen.beta(exp2.everyone.prevstest.beta)
exp2.everyone.prevstest.results <- cbind(exp2.everyone.prevstest.betas,exp2.everyone.prevstest.table,exp2.everyone.prevstest.ci)exp3.everyone.test.table <- gen.m(exp3.1)
exp3.everyone.test.ci <- gen.ci(exp3.1)[3:4,]
exp3.everyone.test.beta <- lmer(scale(loglook)~ type + (1 | subj),
data=exp3.avg)
exp3.everyone.test.betas <- gen.beta(exp3.everyone.test.beta)
exp3.everyone.test.results <- cbind(exp3.everyone.test.betas,exp3.everyone.test.table,exp3.everyone.test.ci)
exp3.everyone.pre.table <- gen.m(exp3.2)
exp3.everyone.pre.ci <- gen.ci(exp3.2)[3:4,]
exp3.everyone.pre.beta <- lmer(scale(loglook) ~ type + (1 | subj),
data=exp3.control)
exp3.everyone.pre.betas <- gen.beta(exp3.everyone.pre.beta)
exp3.everyone.pre.results <- cbind(exp3.everyone.pre.betas,exp3.everyone.pre.table,exp3.everyone.pre.ci)
exp3.everyone.prevstest.table <- gen.m(exp3.3)
exp3.everyone.prevstest.ci <- gen.ci(exp3.3)[3:6,]
exp3.everyone.prevstest.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj), data = exp3.control.test)
exp3.everyone.prevstest.betas <- gen.beta(exp3.everyone.prevstest.beta)
exp3.everyone.prevstest.results <- cbind(exp3.everyone.prevstest.betas,exp3.everyone.prevstest.table,exp3.everyone.prevstest.ci)Experiment 2
Infants looked longer at test when the agent, at test, chose the deeper trench over the shallower trench ([0.07,0.51], ß=0.43, t(29)=2.59, p=.0075, one-tailed). During control events, 13-month-old infants preferred to look at the shallow trench ([-0.37,-0.02], ß=-0.34, t(27.3)=-2.2, p=.036, two-tailed). Their looking preferences significantly differed across the two phases of the experiment, [0.11,0.88], ß=0.75, t(84.74)=2.52, p=.013, two-tailed). These findings accord with those reported in the main text and support the interpretation that infants expected the agent to take the less dangerous action and therefore showed a greater looking preference for the test event than for the control event presenting events over the deeper trench.
Experiment 3
Infants looked longer at test when the agent chose to jump over the deeper trench ([0.09,0.41], ß=-1.05, t(41)=3.06, p=.002, one-tailed). During control events, infants did not show a looking preference for either event ([-0.41,0.17], ß=-0.2, t(68)=-0.82, p=.418, two-tailed). Their looking preferences significantly differed across the test and control trials ([0.04,0.7], ß=0.6, t(108.49)=2.18, p=.032, two-tailed). This finding fully replicates the two key findings from Experiment 2 and accords with the findings reported in the main text.
Order effects in Experiment 1 (reviewer-requested exploratory analysis)
exp1.order <- lmer(data = exp1.avg,
formula = loglook ~ first_fam * type + (1|subj))
exp1.order.table <- gen.m(exp1.order)
exp1.order.ci <- gen.ci(exp1.order)[3:6,]
exp1.order.betas <- lmer(data = exp1.avg,
formula = scale(loglook) ~ first_fam * type + (1|subj))
exp1.order.beta <- gen.beta(exp1.order.betas)
exp1.ordereffects <- cbind(exp1.order.table, exp1.order.beta, exp1.order.ci)Infants’ looking preferences at test did not vary depending on which sequence of events (low to high danger vs high to low danger) they were randomly assigned to watch in the first familiarization trial ([-0.2,0.55], ß=0.29, t(30)=0.93, p=.362, two-tailed). All infants saw both trial orders for 3 familiarization trials each.
Looking time to each familiarization event in Experiment 1 (reviewer-requested exploratory analysis)
detach("package:dplyr", unload = TRUE)
library(dplyr)
exp1.fam <- read.csv("./exp1_fam_csvs/exp1_fam_looks.csv", header=TRUE)
exp1.fam <- exp1.fam %>%
separate(videoclip, into = c("depth", "yesno"), remove=FALSE) %>%
rename(subj= subjID)
exp1.fam$depth <- as.factor(exp1.fam$depth)
exp1.fam$yesno <- as.factor(exp1.fam$yesno)
exp1.fam$trial <- as.factor(exp1.fam$trial)
exp1.fam$subj <- as.factor(exp1.fam$subj)
exp1.fam$videoclip <- as.factor(exp1.fam$videoclip)
exp1.fam.glancedoff <- exp1.fam %>%
mutate(glanced.off = case_when(proportion.on == 1.0 ~ 0,
proportion.on < 1.0 ~ 1))
exp1.fam.glancedoff.totalclips <- exp1.fam.glancedoff %>%
select(subj, depth, videoclip, glanced.off) %>%
group_by(subj, videoclip) %>%
summarise(totalclips = n())
exp1.fam.glancedoff.freq <- exp1.fam.glancedoff %>%
select(subj, depth, videoclip, glanced.off) %>%
group_by(subj, videoclip) %>%
tally(glanced.off)
exp1.fam.glanced.off.summary <- full_join(exp1.fam.glancedoff.totalclips, exp1.fam.glancedoff.freq) %>%
mutate(prop.glancedoff = n/totalclips) %>%
mutate(depth = case_when(videoclip == "deep_no" ~ "deep",
videoclip == "shallow_yes" ~ "shallow",
videoclip == "medium_no" ~ "medium",
videoclip == "medium_yes" ~ "medium"))
exp1.fam.glanced.off.summary$videoclip <- factor(exp1.fam.glanced.off.summary$videoclip, levels=c("shallow_yes", "medium_no", "medium_yes", "deep_no"))figS3 <- exp1.fam.glanced.off.summary %>%
ggplot(aes(videoclip, prop.glancedoff, fill=depth)) +
geom_boxplot() +
geom_point(alpha=0.3) +
geom_line(alpha = 0.2, aes(group = subj)) +
ylab("Proportion of events including look away") +
xlab("Event type") +
# facet_wrap(~depth) +
stat_summary(fun.data =mean_cl_boot, geom="errorbar",width=0.2)+
stat_summary(fun=mean,geom="point",shape=5, size=3)+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
figS3
Figure S3. Proportion of events during which infants glanced away from the screen, relative to how many times infants saw each event. Data come from a random subset of infants in Experiment 1 (N=16 out of 32 total infants), with observations grouped by infant (points connected by grey lines). Error bars represent bootstrapped 95% confidence intervals around the mean. Infants look away from the screen with roughly equal probabilities across the 4 event types.
# compute 4 values per subject, total proportion to
# deep_no, medium_yes, medium_no, shallow_yes
exp1.fam.bymovie <- exp1.fam %>%
group_by(subj,videoclip, depth, yesno) %>%
mutate(proportion.on.total = mean(proportion.on)) %>%
distinct(proportion.on.total)
exp1.fam.bymovie.wide <- exp1.fam.bymovie %>%
pivot_wider(names_from = videoclip, values_from = proportion.on.total, id=subj)
exp1.avg.diff <- exp1.avg %>%
filter(phase == "testavg") %>%
pivot_wider(names_from = type, values_from = look, id=subj) %>%
mutate(delta.look = lower-higher)
exp1.fam.glancedoff.wide <- exp1.fam.glanced.off.summary %>%
pivot_wider(names_from = videoclip, values_from = prop.glancedoff, id=subj)
exp1.famtest <- full_join(exp1.fam.bymovie.wide, exp1.avg.diff, by=c("subj")) %>%
na.omit()
exp1.famtest.long <- exp1.famtest %>%
gather(key = "movie_clip", value = "proportion_looking", shallow_yes:deep_no)
exp1.famtest.glanceoff <- full_join(exp1.fam.glancedoff.wide, exp1.avg.diff, by=c("subj")) %>%
na.omit()
exp1.famtest.glanceofflong <- exp1.famtest.glanceoff %>%
gather(key = "movie_clip", value = "proportion_glanced_off", shallow_yes:deep_no)theme_set(theme_cowplot(font_size=15))
figS4A <-
exp1.famtest.long %>%
ggplot(aes(proportion_looking, delta.look)) +
geom_point() +
geom_smooth(method="lm") +
# geom_line(alpha = 0.2, aes(group = subjID)) +
xlab("Total proportion looking \n to movie clip") +
ylab("Looking preference at test (s)\n
<--- Longer looking to expected ---- Longer looking to unexpected --->") +
facet_wrap(~movie_clip)
figS4A
Figure Sx [not in final SOM] Scatter plot of average proportion looking to each movie clip from familiarization and looking preferences at test.
cor.data <- exp1.famtest.glanceoff %>%
select(shallow_yes:deep_no, delta.look) %>%
rename(VOE_response = delta.look) %>%
as.data.frame()
corrplot(cor(cor.data[,-1]),
method='circle',
type='lower',
addCoef.col ='black',
diag=FALSE)
Figure S4. Correlation plot relating infants’ likelihood of looking away from each of the 4 familiarization events (proportion of events including a look away) to one another, and to infants’ violation of expectation response (unexpected - expected) at test. Values indicate Pearson’s correlations. Descriptively, the more infants looked away from the events, the smaller VOE response they showed at test.
fam.glance1 <- lmer(data = exp1.fam.glanced.off.summary,
formula = prop.glancedoff ~ videoclip + (1|subj))
summary(fam.glance1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: prop.glancedoff ~ videoclip + (1 | subj)
## Data: exp1.fam.glanced.off.summary
##
## REML criterion at convergence: 6.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3654 -0.5786 0.0699 0.7238 1.4625
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.0304 0.174
## Residual 0.0376 0.194
## Number of obs: 64, groups: subj, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.5552 0.0652 37.4836 8.51 2.7e-10 ***
## videoclipmedium_no -0.0208 0.0685 45.0000 -0.30 0.76
## videoclipmedium_yes -0.0333 0.0685 45.0000 -0.49 0.63
## videoclipdeep_no -0.0958 0.0685 45.0000 -1.40 0.17
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vdclpmdm_n vdclpmdm_y
## vidclpmdm_n -0.526
## vdclpmdm_ys -0.526 0.500
## videclpdp_n -0.526 0.500 0.500tab_model(fam.glance1, show.stat=TRUE,show.df=TRUE)| prop glancedoff | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.56 | 0.42 – 0.69 | 8.51 | <0.001 | 58.00 |
| videoclip [medium no] | -0.02 | -0.16 – 0.12 | -0.30 | 0.762 | 58.00 |
| videoclip [medium yes] | -0.03 | -0.17 – 0.10 | -0.49 | 0.629 | 58.00 |
| videoclip [deep no] | -0.10 | -0.23 – 0.04 | -1.40 | 0.167 | 58.00 |
| Random Effects | |||||
| σ2 | 0.04 | ||||
| τ00 subj | 0.03 | ||||
| ICC | 0.45 | ||||
| N subj | 16 | ||||
| Observations | 64 | ||||
| Marginal R2 / Conditional R2 | 0.019 / 0.458 | ||||
plot(allEffects(fam.glance1))
fam.glance2 <- lm(data = exp1.famtest.glanceoff,
formula = scale(delta.look) ~ scale(shallow_yes) + scale(medium_no) + scale(medium_yes) + scale(deep_no))
tab_model(fam.glance2, show.stat=TRUE,show.df=TRUE)| scale(delta look) | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | -0.00 | -0.51 – 0.51 | -0.00 | 1.000 | 11.00 |
| shallow yes | -0.42 | -1.16 – 0.32 | -1.23 | 0.243 | 11.00 |
| medium no | -0.16 | -0.76 – 0.44 | -0.60 | 0.561 | 11.00 |
| medium yes | 0.21 | -0.52 – 0.93 | 0.62 | 0.545 | 11.00 |
| deep no | -0.34 | -0.95 – 0.27 | -1.22 | 0.247 | 11.00 |
| Observations | 16 | ||||
| R2 / R2 adjusted | 0.377 / 0.151 | ||||
summary(fam.glance2)##
## Call:
## lm(formula = scale(delta.look) ~ scale(shallow_yes) + scale(medium_no) +
## scale(medium_yes) + scale(deep_no), data = exp1.famtest.glanceoff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.476 -0.451 -0.215 0.597 1.567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.41e-17 2.30e-01 0.00 1.00
## scale(shallow_yes) -4.15e-01 3.36e-01 -1.23 0.24
## scale(medium_no) -1.64e-01 2.73e-01 -0.60 0.56
## scale(medium_yes) 2.05e-01 3.29e-01 0.62 0.55
## scale(deep_no) -3.37e-01 2.76e-01 -1.22 0.25
##
## Residual standard error: 0.92 on 11 degrees of freedom
## Multiple R-squared: 0.377, Adjusted R-squared: 0.151
## F-statistic: 1.66 on 4 and 11 DF, p-value: 0.228plot(allEffects(fam.glance2))
In Experiment 1, infants, on average, looked longer when the agent jumped deeper trenches for one goal than another, and then chose the other goal later at test. One question is how infants used the information in each of the 4 familiarization events, presented in a looping sequence over 6 familiarization trials, in order to draw this inference. Rather than comparing the relative acceptances and refusals of the agent across 3 different levels of peril (shallow, medium, and deep trenches), one alternative hypothesis is that infants selectively attended when the agent accepted and refused the same obstacle (medium trench) for the two goals, and used this ‘go-no-go’ heuristic to infer that the agent prefers the goal it jumped for, over the goal it refused to jump for.
On this hypothesis, infants should be less likely to glance away from events involving medium trenches (vs the other events), and those who looked away less (i.e. attended more) to the medium trench events should have exhibited larger violation-of-expectation effects at test. To test these predictions, naive coders chose a random 50% of videos from Experiment 1 and annotated the onset and offset times of each iteration of each event in each familiarization loop, ignoring interleaved blank screens, and then annotated the onset and offset of infants’ attention to each event iteration. In the plots and following analyses, the events are named shallow_yes and medium_yes when the agent willingly jumped a shallow or medium trench, and medium_no and deep_no when the agent refused to jump a medium or deep trench. For each infant, we calculated the number of each kind of event they saw. Then, we calculated the proportion of those events that infants looked away from. If an infant looked away from the screen for any portion of the event, we marked that event as one where they looked away. Otherwise, we marked that event as one where they looked For example, if an infant saw 5 deep_no events and glanced away from the screen for 1 of them, this produced a score of 0.2 for that event type, for that infant. We then averaged these proportions within infants across all 4 event types, to produce 4 different proportion glance-away scores per infant. These scores are plotted in Figure S3, are related to each other, and to infants’ looking preferences at test, in Figure S4.
Overall, infants were equally likely to glance away from the screen (vs attend for the entire duration) during the 4 events. See Table S1 for results of the linear mixed effects model (lmer formula: prop.glancedoff ~ videoclip + (1|subj)). Thus, infants did not attend selectively to the events where they had the opportunity to compare the agent’s acceptance and refusal of the medium trench towards the two goals. Instead, they were equally likely to glance away from all 4 types of events.
Table S1. Infants’ probability of glancing away from the 4 video clips from familiarization in Experiment 1
fam1 <- lmer(data = exp1.fam.bymovie,
formula = proportion.on.total ~ videoclip + (1|subj))
summary(fam1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: proportion.on.total ~ videoclip + (1 | subj)
## Data: exp1.fam.bymovie
##
## REML criterion at convergence: -149
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.044 -0.645 0.188 0.665 1.344
##
## Random effects:
## Groups Name Variance Std.Dev.
## subj (Intercept) 0.000997 0.0316
## Residual 0.003324 0.0577
## Number of obs: 64, groups: subj, 16
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.92221 0.01643 51.74040 56.12 <2e-16 ***
## videoclipmedium_no -0.02259 0.02038 45.00000 -1.11 0.27
## videoclipmedium_yes -0.01641 0.02038 45.00000 -0.80 0.43
## videoclipshallow_yes -0.00738 0.02038 45.00000 -0.36 0.72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vdclpmdm_n vdclpmdm_y
## vidclpmdm_n -0.620
## vdclpmdm_ys -0.620 0.500
## vdclpshllw_ -0.620 0.500 0.500tab_model(fam1, show.stat=TRUE, show.df=TRUE)| proportion on total | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.92 | 0.89 – 0.96 | 56.12 | <0.001 | 58.00 |
| videoclip [medium no] | -0.02 | -0.06 – 0.02 | -1.11 | 0.272 | 58.00 |
| videoclip [medium yes] | -0.02 | -0.06 – 0.02 | -0.80 | 0.424 | 58.00 |
| videoclip [shallow yes] | -0.01 | -0.05 – 0.03 | -0.36 | 0.719 | 58.00 |
| Random Effects | |||||
| σ2 | 0.00 | ||||
| τ00 subj | 0.00 | ||||
| ICC | 0.23 | ||||
| N subj | 16 | ||||
| Observations | 64 | ||||
| Marginal R2 / Conditional R2 | 0.017 / 0.244 | ||||
plot(allEffects(fam1))
We then tested the second prediction: that infants who glanced away from the medium trench events (i.e. those who missed the critical information for a go-no-go strategy) would also show smaller violation-of-expectation responses at test. To do this, we calculated infants’ looking preference at test (average duration looking when the agent chose the less valued goal, minus average duration looking when the agent chose the more valued goal), and asked whether variability in infants’ looking behavior towards each of the 4 events predicted variability in these looking preferences. We found that infants’ tendency to glance away from the events involving medium trenches, or towards any of the 4 events, did not predict the magnitude of their violation-of-expectation response. See Table S2 for full results (lm formula: delta.look ~ shallow_yes + medium_no + medium_yes + deep_no).
Together, these findings suggest that infants did not selectively attend to the videos with the same trench depth during familiarization in Experiment 1 (or selectively glance away from the other events), and that their looking towards these videos did not predict stronger inferences about which goal was more valuable. Therefore, it appears unlikely that infants as a group used a “go-no-go” heuristic on the agent’s actions over the medium trenches in order to infer which the agent preferred. To be clear, we are not suggesting that infants could never use such a strategy. Instead we are suggesting that this strategy does not appear to explain the results of Experiment 1 (based on this analysis), or the results of Experiments 2-3 (in principle, based on the experimental design, in which the agent always accepts and never refuses jumping actions)
Table S2. Infants’ violation of expectation responses at test, as predicted by their tendency to glance away from the 4 video clips from familiarization in Experiment 1. Dependent and independent variables were z-scored prior to entry into the model.
fam2 <- lm(data = exp1.famtest,
formula = scale(delta.look) ~ scale(shallow_yes) + scale(medium_no) + scale(medium_yes) + scale(deep_no))
tab_model(fam2, show.stat=TRUE,show.df=TRUE)| scale(delta look) | |||||
|---|---|---|---|---|---|
| Predictors | Estimates | CI | Statistic | p | df |
| (Intercept) | 0.00 | -0.49 – 0.49 | 0.00 | 1.000 | 11.00 |
| shallow yes | -0.01 | -0.56 – 0.55 | -0.03 | 0.973 | 11.00 |
| medium no | 0.12 | -0.44 – 0.68 | 0.48 | 0.643 | 11.00 |
| medium yes | 0.33 | -0.31 – 0.97 | 1.13 | 0.284 | 11.00 |
| deep no | 0.37 | -0.19 – 0.94 | 1.45 | 0.176 | 11.00 |
| Observations | 16 | ||||
| R2 / R2 adjusted | 0.419 / 0.208 | ||||
summary(fam2)##
## Call:
## lm(formula = scale(delta.look) ~ scale(shallow_yes) + scale(medium_no) +
## scale(medium_yes) + scale(deep_no), data = exp1.famtest)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8595 -0.3005 0.0388 0.3617 1.1282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.67e-16 2.22e-01 0.00 1.00
## scale(shallow_yes) -8.69e-03 2.53e-01 -0.03 0.97
## scale(medium_no) 1.20e-01 2.53e-01 0.48 0.64
## scale(medium_yes) 3.29e-01 2.92e-01 1.13 0.28
## scale(deep_no) 3.74e-01 2.59e-01 1.45 0.18
##
## Residual standard error: 0.89 on 11 degrees of freedom
## Multiple R-squared: 0.419, Adjusted R-squared: 0.208
## F-statistic: 1.98 on 4 and 11 DF, p-value: 0.166plot(allEffects(fam2))
# sim <- powerCurve(extend(exp1.1, along="subj", n=500),
# along="subj", breaks = c(36, 40, 44, 48, 52, 56, 60, 64, 68), alpha = .05, seed = 123)
# plot(sim)
# print(sim)# reliability <- wide %>% filter(reliability ==1) %>%
# select(subj, sex, experiment, test1, test2, test3, test4) %>%
# gather(trial, look, test1:test4) %>%
# mutate(trialn = str_remove(trial, "test")) %>%
# group_by(subj, trialn)
# write.csv(reliability, "risk_rel.csv")